Robotic vehicle active safety systems and methods

ABSTRACT

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 14/932,962, filed on Nov. 4, 2015, which is relatedto U.S. patent application Ser. No. 14/932,959 filed Nov. 4, 2015entitled “Autonomous Vehicle Fleet Service and System,” and U.S. patentapplication Ser. No. 14/932,963 filed Nov. 4, 2015 entitled “AdaptiveMapping to Navigate Autonomous Vehicles Responsive To PhysicalEnvironment Changes,” all of which are hereby incorporated by referencein their entirety for all purposes.

FIELD

Embodiments of the present application relate generally to methods,systems and apparatus for safety systems in robotic vehicles.

BACKGROUND

Autonomous vehicles, such as the type designed to transport passengersor cargo, for example, may be designed to autonomously navigate acomputed trajectory (e.g., a computed path). One operational objectiveof the autonomous vehicle should be to avoid collisions with othervehicles, pedestrians or other obstacles that may be encountered duringoperation of the autonomous vehicle. However, the autonomous vehicle mayshare the road with other vehicles, pedestrians and other obstacleswhich may by their actions create situations that may result in apotential collision with the autonomous vehicle or otherwise threatenthe safety of passengers in the autonomous vehicle. As one example, avehicle approaching the autonomous vehicle while the autonomous vehicleis stopped at an intersection may rear-end the autonomous vehicle due toinattention of the driver of the approaching vehicle.

Accordingly, there is a need for systems and methods to implement activesafety systems in an autonomous vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) are disclosed in thefollowing detailed description and the accompanying drawings:

FIG. 1 depicts one example of a system for implementing an active safetysystem in an autonomous vehicle;

FIG. 2A depicts one example of a flow diagram for implementing an activesafety system in an autonomous vehicle;

FIG. 2B depicts another example of a flow diagram for implementing anactive safety system in an autonomous vehicle;

FIG. 2C depicts yet another example of a flow diagram for implementingan active safety system in an autonomous vehicle;

FIG. 3A depicts one example of a system for implementing an activesafety system in an autonomous vehicle;

FIG. 3B depicts another example of a system for implementing an activesafety system in an autonomous vehicle;

FIG. 4 depicts one example of a flow diagram for implementing aperception system in an autonomous vehicle;

FIG. 5 depicts one example of object prioritization by a planner systemin an autonomous vehicle;

FIG. 6 depicts a top plan view of one example of threshold locations andassociated escalating alerts in an active safety system in an autonomousvehicle;

FIG. 7 depicts one example of a flow diagram for implementing a plannersystem in an autonomous vehicle;

FIG. 8 depicts one example of a block diagram of systems in anautonomous vehicle;

FIG. 9 depicts a top view of one example of an acoustic beam-steeringarray in an exterior safety system of an autonomous vehicle;

FIG. 10A depicts top plan views of two examples of sensor coverage;

FIG. 10B depicts top plan views of another two examples of sensorcoverage;

FIG. 11A depicts one example of an acoustic beam-steering array in anexterior safety system of an autonomous vehicle;

FIG. 11B depicts one example of a flow diagram for implementing acousticbeam-steering in an autonomous vehicle;

FIG. 11C depicts a top plan view of one example of an autonomous vehiclesteering acoustic energy associated with an acoustic alert to an object;

FIG. 12A depicts one example of a light emitter in an exterior safetysystem of an autonomous vehicle;

FIG. 12B depicts profile views of examples of light emitters in anexterior safety system of an autonomous vehicle;

FIG. 12C depicts a top plan view of one example of light emitteractivation based on an orientation of an autonomous vehicle relative toan object;

FIG. 12D depicts a profile view of one example of light emitteractivation based on an orientation of an autonomous vehicle relative toan object;

FIG. 12E depicts one example of a flow diagram for implementing a visualalert from a light emitter in an autonomous vehicle;

FIG. 12F depicts a top plan view of one example of a light emitter of anautonomous vehicle emitting light to implement a visual alert;

FIG. 13A depicts one example of a bladder system in an exterior safetysystem of an autonomous vehicle;

FIG. 13B depicts examples of bladders in an exterior safety system of anautonomous vehicle;

FIG. 13C depicts examples of bladder deployment in an autonomousvehicle;

FIG. 14 depicts one example of a seat belt tensioning system in aninterior safety system of an autonomous vehicle;

FIG. 15 depicts one example of a seat actuator system in an interiorsafety system of an autonomous vehicle;

FIG. 16A depicts one example of a drive system in an autonomous vehicle;

FIG. 16B depicts one example of obstacle avoidance maneuvering in anautonomous vehicle; and

FIG. 16C depicts another example of obstacle avoidance maneuvering in anautonomous vehicle.

Although the above-described drawings depict various examples of theinvention, the invention is not limited by the depicted examples. It isto be understood that, in the drawings, like reference numeralsdesignate like structural elements. Also, it is understood that thedrawings are not necessarily to scale.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, a method, an apparatus, a userinterface, software, firmware, logic, circuitry, or a series ofexecutable program instructions embodied in a non-transitory computerreadable medium. Such as a non-transitory computer readable medium or acomputer network where the program instructions are sent over optical,electronic, or wireless communication links and stored or otherwisefixed in a non-transitory computer readable medium. Examples of anon-transitory computer readable medium includes but is not limited toelectronic memory, RAM, DRAM, SRAM, ROM, EEPROM, Flash memory,solid-state memory, hard disk drive, and non-volatile memory, forexample. One or more non-transitory computer readable mediums may bedistributed over a number of devices. In general, operations ofdisclosed processes may be performed in an arbitrary order, unlessotherwise provided in the claims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims and numerousalternatives, modifications, and equivalents are encompassed. Numerousspecific details are set forth in the following description in order toprovide a thorough understanding. These details are provided for thepurpose of example and the described techniques may be practicedaccording to the claims without some or all of these specific details.For clarity, technical material that is known in the technical fieldsrelated to the examples has not been described in detail to avoidunnecessarily obscuring the description.

FIG. 1 depicts one example of a system for implementing an active safetysystem in an autonomous vehicle. In FIG. 1, an autonomous vehicle 100(depicted in top plan view) may be travelling through an environment 190external to the autonomous vehicle 100 along a trajectory 105. Forpurposes of explanation, environment 190 may include one or more objectsthat may potentially collide with the autonomous vehicle 100, such asstatic and/or dynamic objects, or objects that pose some other danger topassengers (not shown) riding in the autonomous vehicle 100 and/or tothe autonomous vehicle 100. For example, in FIG. 1, an object 180 (e.g.,an automobile) is depicted as having a trajectory 185, that if notaltered (e.g., by changing trajectory, slowing down, etc.), may resultin a potential collision 187 with the autonomous vehicle 100 (e.g., byrear-ending the autonomous vehicle 100).

Autonomous vehicle 100 may use a sensor system (not shown) to sense(e.g., using passive and/or active sensors) the environment 190 todetect the object 180 and may take action to mitigate or prevent thepotential collision of the object 180 with the autonomous vehicle 100.An autonomous vehicle system 101 may receive sensor data 132 from thesensor system and may receive autonomous vehicle location data 139(e.g., implemented in a localizer system of the autonomous vehicle 100).The sensor data 132 may include but is not limited to data representinga sensor signal (e.g., a signal generated by a sensor of the sensorsystem). The data representing the sensor signal may be indicative ofthe environment 190 external to the autonomous vehicle 100. Theautonomous vehicle location data 139 may include but is not limited todata representing a location of the autonomous vehicle 100 in theenvironment 190. As one example, the data representing the location ofthe autonomous vehicle 100 may include position and orientation data(e.g., a local position or local pose), map data (e.g., from one or moremap tiles), data generated by a global positioning system (GPS) and datagenerated by an inertial measurement unit (IMU). In some examples, asensor system of the autonomous vehicle 100 may include a globalpositioning system, an inertial measurement unit, or both.

Autonomous vehicle system 101 may include but is not limited tohardware, software, firmware, logic, circuitry, computer executableinstructions embodied in a non-transitory computer readable medium, orany combination of the foregoing, to implement a path calculator 112, anobject data calculator 114 (e.g., implemented in a perception system ofthe autonomous vehicle 100), a collision predictor 116, an objectclassification determinator 118 and a kinematics calculator 115.Autonomous vehicle system 101 may access one or more data storesincluding but not limited to an objects type data store 119. Objecttypes data store 119 may include data representing object typesassociated with object classifications for objects detected inenvironment 190 (e.g., a variety of pedestrian object types such as“sitting”, “standing” or “running”, may be associated with objectsclassified as pedestrians).

Path calculator 112 may be configured to generate data representing atrajectory of the autonomous vehicle 100 (e.g., trajectory 105), usingdata representing a location of the autonomous vehicle 100 in theenvironment 190 and other data (e.g., local pose data included invehicle location data 139), for example. Path calculator 112 may beconfigured to generate future trajectories to be executed by theautonomous vehicle 100, for example. In some examples, path calculator112 may be implanted in or as part of a planner system of the autonomousvehicle 100. In other examples, the path calculator 112 and/or theplanner system may calculate data associated with a predicted motion ofan object in the environment and may determine a predicted object pathassociated with the predicted motion of the object. In some examples,the object path may constitute the predicted object path. In otherexamples, the object path may constitute a predicted object trajectory.In yet other examples, the object path (e.g., in the environment) mayconstitute a predicted object trajectory that may be identical to orsimilar to a predicted object trajectory.

Object data calculator 114 may be configured to calculate datarepresenting the location of the object 180 disposed in the environment190, data representing an object track associated with the object 180,and data representing an object classification associated with theobject 180, and the like. Object data calculator 114 may calculate thedata representing the location of the object, the data representing theobject track, and the data representing the object classification usingdata representing a sensor signal included in sensor data 132, forexample. In some examples, the object data calculator 114 may beimplemented in or may constitute a perception system, or a portionthereof, being configured to receive the data representing the sensorsignal (e.g., a sensor signal from a sensor system).

Object classification determinator 118 may be configured to access datarepresenting object types 119 (e.g., a species of an objectclassification, a subclass of an object classification, or a subset ofan object classification) and may be configured to compare the datarepresenting the object track and the data representing the objectclassification with the data representing the object types 119 todetermine data representing an object type (e.g., a species or subclassof the object classification). As one example, a detected object havingan object classification of a “car” may have an object type of “sedan”,“coupe”, “truck” or “school bus”. An object type may include additionalsubclasses or subsets such as a “school bus” that is parked may have anadditional subclass of “static” (e.g. the school bus is not in motion),or an additional subclass of “dynamic” (e.g. the school bus is inmotion), for example.

Collision predictor 116 may be configured to use the data representingthe object type, the data representing the trajectory of the object andthe data representing the trajectory of the autonomous vehicle topredict a collision (e.g., 187) between the autonomous vehicle 100 andthe object 180, for example.

A kinematics calculator 115 may be configured to compute datarepresenting one or more scalar and/or vector quantities associated withmotion of the object 180 in the environment 190, including but notlimited to velocity, speed, acceleration, deceleration, momentum, localpose and force, for example. Data from kinematics calculator 115 may beused to compute other data including but not limited to datarepresenting an estimated time to impact between the object 180 and theautonomous vehicle 100 and data representing a distance between theobject 180 and the autonomous vehicle 100, for example. In some examplesthe kinematics calculator 115 may be configured to predict a likelihoodthat other objects in the environment 190 (e.g. cars, pedestrians,bicycles, motorcycles, etc.) are in an alert or in-control state, versusan un-alert, out-of-control, or drunk state, etc. As one example, thekinematics calculator 115 may be configured estimate a probability thatother agents (e.g., drivers or riders of other vehicles) are behavingrationally (e.g., based on motion of the object they are driving orriding), which may dictate behavior of the autonomous vehicle 100,versus behaving irrationally (e.g. based on erratic motion of the objectthey are riding or driving). Rational or irrational behavior may beinferred based on sensor data received over time that may be used toestimate or predict a future location of the object relative to acurrent or future trajectory of the autonomous vehicle 100.Consequently, a planner system of the autonomous vehicle 100 may beconfigured to implement vehicle maneuvers that are extra cautious and/oractivate a safety system of the autonomous vehicle 100, for example.

A safety system activator 120 may be configured to activate one or moresafety systems of the autonomous vehicle 100 when a collision ispredicted by the collision predictor 116 and/or the occurrence of othersafety related events (e.g., an emergency maneuver by the vehicle 100,such as hard braking, sharp acceleration, etc.). Safety system activator120 may be configured to activate an interior safety system 122, anexterior safety system 124, a drive system 126 (e.g., cause drive system126 to execute an emergency maneuver to avoid the collision), or anycombination of the foregoing. For example, drive system 126 may receivedata being configured to cause a steering system (e.g., set a steeringangle or a steering vector for the wheels) and a propulsion system(e.g., power supplied to an electric motor) to alter the trajectory ofvehicle 100 from trajectory 105 to a collision avoidance trajectory 105a.

FIG. 2A depicts one example of a flow diagram 200 for implementing anactive safety system in an autonomous vehicle 100. In flow diagram 200,at a stage 202, data representing a trajectory 203 of an autonomousvehicle 100 in an environment external to the autonomous vehicle 100(e.g., environment 190) may be received (e.g., implemented in a plannersystem of the autonomous vehicle 100).

At a stage 204, object data associated with an object (e.g., automobile180) disposed in the environment (e.g., environment 190) may becalculated. Sensor data 205 may be accessed at the stage 204 tocalculate the object data. The object data may include but is notlimited to data representing object location in the environment, anobject track associated with the object (e.g., static for a non-movingobject and dynamic for an object in motion), and an objectclassification (e.g., a label) associated with the object (e.g.,pedestrian, dog, cat, bicycle, motorcycle, automobile, truck, etc.). Thestage 204 may output one or more types of data associated with anobject, including but not limited to data representing object location207 in the environment, data representing an object track 209, and datarepresenting an object classification 211.

At a stage 206 a predicted object path of the object in the environmentmay be calculated. As one example, the stage 206 may receive the datarepresenting object location 207 and may process that data to generatedata representing a predicted object path 213.

At a stage 208, data representing object types 215 may be accessed, andat a stage 210, data representing an object type 217 may be determinedbased on the data representing the object track 209, the datarepresenting the object classification 211 and the data representingobject types 215. Examples of an object type may include but are notlimited to a pedestrian object type having a static object track (e.g.,the pedestrian is not in motion), an automobile object type having adynamic object track (e.g., the automobile is in motion) and aninfrastructure object type having a static object track (e.g., a trafficsign, a lane marker, a fire hydrant), etc., just to name a few. Thestage 210 may output the data representing object type 217.

At a stage 212 a collision between the autonomous vehicle and the objectmay be predicted based on the determined object type 217, the autonomousvehicle trajectory 203 and the predicted object path 213. As oneexample, a collision may be predicted based in part on the determinedobject type 217 due to the object having an object track that is dynamic(e.g., the object is in motion in the environment), the trajectory ofthe object being in potential conflict with a trajectory of theautonomous vehicle (e.g., the trajectories may intersect or otherwiseinterfere with each other), and the object having an objectclassification 211 (e.g., used in computing the object type 217) thatindicates the object is a likely collision threat (e.g., the object isclassified as an automobile, a skateboarder, a bicyclists, a motorcycle,etc.).

At a stage 214, a safety system of the autonomous vehicle may beactivated when the collision is predicted (e.g., at the stage 212). Thestage 214 may activate one or more safety systems of the autonomousvehicle, such as one or more interior safety systems, one or moreexterior safety systems, one or more drive systems (e.g., steering,propulsion, braking, etc.) or a combination of the foregoing, forexample. The stage 214 may cause (e.g., by communicating data and/orsignals) a safety system activator 220 to activate one or more of thesafety systems of the autonomous vehicle 100.

FIG. 2B depicts another example of a flow diagram 250 for implementingan active safety system in an autonomous vehicle 100. In flow diagram250, at a stage 252, data representing the trajectory 253 of anautonomous vehicle 100 in an environment external to the autonomousvehicle 100 (e.g., environment 190) may be received (e.g., from aplanner system of the autonomous vehicle 100).

At a stage 254, a location of an object in the environment may bedetermined. Sensor data 255 may be processed (e.g., by a perceptionsystem) to determine data representing an object location in theenvironment 257. Data associated with an object (e.g., object dataassociated with object 180) in the environment (e.g., environment 190)may be determined at the state 254. Sensor data 255 accessed at thestage 254 may be used to determine the object data. The object data mayinclude but is not limited to data representing a location of the objectin the environment, an object track associated with the object (e.g.,static for a non-moving object and dynamic for an object in motion), anobject classification associated with the object (e.g., pedestrian, dog,cat, bicycle, motorcycle, automobile, truck, etc.) and an object typeassociated with the object. The stage 254 may output one or more typesof data associated with an object, including but not limited to datarepresenting the object location 257 in the environment, datarepresenting an object track 261 associated with the object, datarepresenting an object classification 263 associated with the object,and data representing an object type 259 associated with the object.

At a stage 256 a predicted object path of the object in the environmentmay be calculated. As one example, the stage 256 may receive the datarepresenting the object location 257 and may process that data togenerate data representing a predicted object path 265. In someexamples, the data representing the predicted object path 265, generatedat the stage 256, may be used as a data input at another stage of flowdiagram 250, such as at a stage 258. In other examples, the stage 256may be bypassed and flow diagram 250 may transition from the stage 254to the stage 258.

At the stage 258 a collision between the autonomous vehicle and theobject may be predicted based the autonomous vehicle trajectory 253 andthe object location 265. The object location 257 may change from a firstlocation to a next location due to motion of the object in theenvironment. For example, at different points in time, the object may bein motion (e.g., has an object track of dynamic “D”), may be motionless(e.g., has an object track of static “S”), or both. However, theperception system may continually track the object (e.g., using sensordata from the sensor system) during those different points in time todetermine object location 257 at the different point in times. Due tochanges in a direction of motion of the object and/or the objectswitching between being in motion and being motionless, the predictedobject path 265 calculated at the stage 256 may be difficult todetermine; therefore the predicted object path 265 need not be used as adata input at the stage 258.

The stage 258 may predict the collision using data not depicted in FIG.2B, such as object type 259 and predicted object path 265, for example.As a first example, at the stage 258, a collision between the autonomousvehicle and the object may be predicted based on the autonomous vehicletrajectory 253, the object location 257 and the object type 259. Assecond example, at the stage 258, a collision between the autonomousvehicle and the object may be predicted based on the autonomous vehicletrajectory 253 and the predicted object path 265. As a third example, atthe stage 258, a collision between the autonomous vehicle and the objectmay be predicted based on the autonomous vehicle trajectory 253, thepredicted object path 265 and the object type 259.

At a stage 260, a safety system of the autonomous vehicle may beactivated when the collision is predicted (e.g., at the stage 258). Thestage 260 may activate one or more safety systems of the autonomousvehicle, such as one or more interior safety systems, one or moreexterior safety systems, one or more drive systems (e.g., steering,propulsion, braking, etc.) or a combination of the foregoing, forexample. The stage 260 may cause (e.g., by communicating data and/orsignals) a safety system activator 269 to activate one or more of thesafety systems of the autonomous vehicle.

FIG. 2C depicts yet another example of a flow diagram 270 forimplementing an active safety system in an autonomous vehicle. At astage 272, data representing the trajectory 273 of an autonomous vehicle100 in an environment external to the autonomous vehicle 100 (e.g.,environment 190) may be received (e.g., from a planner system of theautonomous vehicle 100).

At a stage 274 a location of an object in the environment may bedetermined (e.g., by a perception system) using sensor data 275, forexample. The stage 274 may generate data representing object location279. The data representing the object location 279 may include datarepresenting a predicted rate of motion 281 of the object relative tothe location of the object in the environment. For example, if theobject has a static object track indicative of no motion in theenvironment, then the predictive rate of motion 281 may be zero.However, if the object track of the object is dynamic and the objectclassification is an automobile, then the predicted rate of motion 281may be non-zero.

At a stage 276 a predicted next location of the object in theenvironment may be calculated based on the predicted rate of motion 281.The stage 276 may generate data representing the predicted next location283.

At a stage 278, probabilities of impact between the object and theautonomous vehicle may be predicted based on the predicted next location283 and the autonomous vehicle trajectory 273. The stage 278 maygenerate data representing the probabilities of impact 285.

At a stage 280, subsets of thresholds (e.g., a location or a distance inthe environment) to activate different escalating functions of subsetsof safety systems of the autonomous vehicle may be calculated based onthe probabilities of impact 285. At least one subset of the thresholdsbeing associated with the activation of different escalating functionsof a safety system of the autonomous vehicle. The stage 280 may generatedata representing one or more threshold subsets 287. In some examples,the subsets of thresholds may constitute a location relative to theautonomous vehicle or may constitute a distance relative to theautonomous vehicle. For example, a threshold may be a function of alocation or a range of locations relative to a reference location (e.g.,the autonomous vehicle). Further, a threshold may be a function ofdistance relative to an object and the autonomous vehicle, or betweenany objects or object locations, including distances between predictedobject locations.

At a stage 282, one or more of the different escalating functions of thesafety system may be activated based on an associated predictedprobability (e.g., activation of a bladder based on a predicted set ofprobabilities of impact indicative of an eminent collision). The stage282 may cause (e.g., by communicating data and/or signals) a safetysystem activator 289 to activate one or more of the safety systems ofthe autonomous vehicle based on corresponding one or more sets ofprobabilities of collision.

FIG. 3A depicts one example 300 of a system for implementing an activesafety system in an autonomous vehicle. In FIG. 3A, autonomous vehiclesystem 301 may include a sensor system 320 including sensors 328 beingconfigured to sense the environment 390 (e.g., in real-time or innear-real-time) and generate (e.g., in real-time) sensor data 332 and334 (e.g., data representing a sensor signal).

Autonomous vehicle system 301 may include a perception system 340 beingconfigured to detect objects in environment 390, determine an objecttrack for objects, classify objects, track locations of objects inenvironment 390, and detect specific types of objects in environment390, such as traffic signs/lights, road markings, lane markings and thelike, for example. Perception system 340 may receive the sensor data 334from a sensor system 320.

Autonomous vehicle system 301 may include a localizer system 330 beingconfigured to determine a location of the autonomous vehicle in theenvironment 390. Localizer system 330 may receive sensor data 332 from asensor system 320. In some examples, sensor data 332 received bylocalizer system 330 may not be identical to the sensor data 334received by the perception system 340. For example, perception system330 may receive data 334 from sensors including but not limited to LIDAR(e.g., 2D, 3D, color LIDAR), RADAR, and Cameras (e.g., image capturedevices); whereas, localizer system 330 may receive data 332 includingbut not limited to global positioning system (GPS) data, inertialmeasurement unit (IMU) data, map data, route data, Route NetworkDefinition File (RNDF) data and map tile data. Localizer system 330 mayreceive data from sources other than sensor system 320, such as a datastore, data repository, memory, etc. In other examples, sensor data 332received by localizer system 330 may be identical to the sensor data 334received by the perception system 340. In various examples, localizersystem 330 and perception system 340 may or may not implement similar orequivalent sensors or types of sensors. Further, localizer system 330and perception system 340 each may implement any type of sensor data 332independently of each other.

Perception system 340 may process sensor data 334 to generate objectdata 349 that may be received by a planner system 310. Object data 349may include data associated with objects detected in environment 390 andthe data may include but is not limited to data representing objectclassification, object type, object track, object location, predictedobject path, predicted object trajectory, and object velocity, forexample.

Localizer system 330 may process sensor data 334, and optionally, otherdata, to generate position and orientation data, local pose data 339that may be received by the planner system 310. The local pose data 339may include, but is not limited to, data representing a location of theautonomous vehicle in the environment 390, GPS data, IMU data, map data,route data, Route Network Definition File (RNDF) data, odometry data,wheel encoder data, and map tile data, for example.

Planner system 310 may process the object data 349 and the local posedata 339 to compute a path (e.g., a trajectory of the autonomousvehicle) for the autonomous vehicle through the environment 390. Thecomputed path being determined in part by objects in the environment 390that may create an obstacle to the autonomous vehicle and/or may pose acollision threat to the autonomous vehicle, for example.

Planner system 310 may be configured to communicate control and data 317with one or more vehicle controllers 350. Control and data 317 mayinclude information configured to control driving operations of theautonomous vehicle (e.g., steering, braking, propulsion, signaling,etc.) via a drive system 326, to activate one or more interior safetysystems 322 of the autonomous vehicle and to activate one or moreexterior safety systems 324 of the autonomous vehicle. Drive system 326may perform additional functions associated with active safety of theautonomous vehicle, such as collision avoidance maneuvers, for example.

Vehicle controller(s) 350 may be configured to receive the control anddata 317, and based on the control and data 317, communicate interiordata 323, exterior data 325 and drive data 327 to the interior safetysystem 322, the exterior safety system 324, and the drive system 326,respectively, as determined by the control and data 317, for example. Asone example, if planner system 310 determines that the interior safetysystem 322 is to be activated based on some action of an object inenvironment 390, then control and data 317 may include informationconfigured to cause the vehicle controller 350 to generate interior data323 to activate one or more functions of the interior safety system 322.

The autonomous vehicle system 301 and its associated systems 310, 320,330, 340, 350, 322, 324 and 326 may be configured to access data 315from a data store 311 (e.g., a data repository) and/or data 312 from anexternal resource 313 (e.g., the Cloud, the Internet, a wirelessnetwork). The autonomous vehicle system 301 and its associated systems310, 320, 330, 340, 350, 322, 324 and 326 may be configured to access,in real-time, data from a variety of systems and/or data sourcesincluding but not limited to those depicted in FIG. 3A. As one example,localizer system 330 and perception system 340 may be configured toaccess in real-time the sensor data 332 and the sensor data 334. Asanother example, the planner system 310 may be configured to access inreal-time the object data 349, the local pose data 339 and control anddata 317. In other examples, the planner system 310 may be configured toaccess in real-time the data store 311 and/or the external resource 313.

FIG. 3B depicts another example 399 of a system for implementing anactive safety system in an autonomous vehicle. In example 399, sensors328 in sensor system 320 may include but are not limited to one or moreof: Light Detection and Ranging sensors 371 (LIDAR); image capturesensors 373 (e.g., Cameras); Radio Detection And Ranging sensors 375(RADAR); sound capture sensors 377 (e.g., Microphones); GlobalPositioning System sensors (GPS) and/or Inertial Measurement Unitsensors (IMU) 379; and Environmental sensor(s) 372 (e.g., temperature,barometric pressure), for example. Localizer system 330 and perceptionsystem 340 may receive sensor data 332 and/or sensor data 334,respectively, from one or more of the sensors 328. For example,perception system 340 may receive sensor data 334 relevant to determineinformation associated with objects in environment 390, such as sensordata from LIDAR 371, Cameras 373, RADAR 375, Environmental 372, andMicrophones 377; whereas, localizer system 330 may receive sensor data332 associated with the location of the autonomous vehicle inenvironment 390, such as from GPS/IMU 379. Further, localizer system 330may receive data from sources other than the sensor system 320, such asmap data, map tile data, route data, Route Network Definition File(RNDF) data, a data store, a data repository, etc., for example. In someexamples, sensor data (332, 334) received by localizer system 330 may beidentical to the sensor data (332, 334) received by the perceptionsystem 340. In other examples, sensor data (332, 334) received bylocalizer system 330 may not be identical to the sensor data (332, 334)received by the perception system 340. Sensor data 332 and 334 each mayinclude data from any combination of one or more sensors or sensor typesin sensor system 320. The amounts and types of sensor data 332 and 334may be independent from the other and may or may not be similar orequivalent.

As one example, localizer system 330 may receive and/or access data fromsources other than sensor data (332, 334) such as odometry data 336 frommotion sensors to estimate a change in position of the autonomousvehicle 100 over time, wheel encoders 337 to calculate motion, distanceand other metrics of the autonomous vehicle 100 based on wheel rotations(e.g., by propulsion system 368), map data 335 from data representingmap tiles, route data, Route Network Definition File (RNDF) data and/orothers, and data representing an autonomous vehicle (AV) model 338 thatmay be used to calculate vehicle location data based on models ofvehicle dynamics (e.g., from simulations, captured data, etc.) of theautonomous vehicle 100. Localizer system 330 may use one or more of thedata resources depicted to generate data representing local pose data339.

As another example, perception system 340 may parse or otherwiseanalyze, process, or manipulate sensor data (332, 334) to implementobject detection 341, object track 343 (e.g., determining which detectedobjects are static (no motion) and which are dynamic (in motion)),object classification 345 (e.g., cars, motorcycle, bike, pedestrian,skate boarder, mailbox, buildings, street lights, etc.), object tracking347 (e.g., tracking an object based on changes in a location of theobject in the environment 390), and traffic light/sign detection 342(e.g., stop lights, stop signs, rail road crossings, lane markers,pedestrian cross-walks, etc.).

As yet another example, planner system 310 may receive the local posedata 339 and the object data 349 and may parse or otherwise analyze,process, or manipulate data (local pose data 339, object data 349) toimplement functions including but not limited to trajectory calculation381, threshold location estimation 386, audio signal selection 389,light pattern selection 382, kinematics calculation 384, object typedetection 387, collision prediction 385 and object data calculation 383,for example. Planner system 310 may communicate trajectory and controldata 317 to a vehicle controller(s) 350. Vehicle controller(s) 350 mayprocess the vehicle control and data 317 to generate drive system data327, interior safety system data 323 and exterior safety system data325. Drive system data 327 may be communicated to a drive system 326.Drive system 326 may communicate the drive system data 327 to a brakingsystem 364, a steering system 366, a propulsion system 368, and a signalsystem 362 (e.g., turn signals, brake signals, headlights, and runninglights). For example, drive system data 327 may include steering angledata for steering system 366 (e.g., a steering angle for a wheel),braking data for brake system 364 (e.g., brake force to be applied to abrake pad), and propulsion data (e.g., a voltage, current or power to beapplied to a motor) for propulsion system 368. A dashed line 377 mayrepresent a demarcation between a vehicle trajectory processing layerand a vehicle physical execution layer where data processed in thevehicle trajectory processing layer is implemented by one or more of thedrive system 326, the interior safety system 322 or the exterior safetysystem 324. As one example, one or more portions of the interior safetysystem 322 may be configured to enhance the safety of passengers in theautonomous vehicle 100 in the event of a collision and/or other extremeevent (e.g., a collision avoidance maneuver by the autonomous vehicle100). As another example, one or more portions of the exterior safetysystem 324 may be configured to reduce impact forces or negative effectsof the aforementioned collision and/or extreme event.

Interior safety system 322 may have systems including but not limited toa seat actuator system 363 and a seat belt tensioning system 361.Exterior safety system 324 may have systems including but not limited toan acoustic array system 365, a light emitter system 367 and a bladdersystem 369. Drive system 326 may have systems including but not limitedto a braking system 364, a signal system 362, a steering system 366 anda propulsion system 368. Systems in exterior safety system 324 may beconfigured to interface with the environment 390 by emitting light intothe environment 390 using one or more light emitters (not shown) in thelight emitter system 367, emitting a steered beam of acoustic energy(e.g., sound) into the environment 390 using one or more acousticbeam-steering arrays (not shown) in the acoustic beam-steering array 365or by expanding one or more bladders (not shown) in the bladder system369 from an un-deployed position to a deployed position, or anycombination of the foregoing. Further, the acoustic beam-steering array365 may emit acoustic energy into the environment using transducers, airhorns, or resonators, for example. The acoustic energy may beomnidirectional, or may constitute a steered beam, or otherwise focusedsound (e.g., a directional acoustic source, a phased array, a parametricarray, a large radiator, of ultrasonic source). Accordingly, systems inexterior safety system 324 may be positioned at one or more locations ofthe autonomous vehicle 100 configured to allow the systems to interfacewith the environment 390, such as a location associated with an externalsurface (e.g., 100 e in FIG. 1) of the autonomous vehicle 100. Systemsin interior safety system 322 may be positioned at one or more locationsassociated with an interior (e.g., 100 i in FIG. 1) of the autonomousvehicle 100 and may be connected with one or more structures of theautonomous vehicle 100, such as a seat, a bench seat, a floor, a rail, abracket, a pillar, or other structure. The seat belt tensioning system361 and the seat actuator system 363 may be coupled with one or morestructures configured to support mechanical loads that may occur due toa collision, vehicle acceleration, vehicle deceleration, evasivemaneuvers, sharp turns, hard braking, etc., for example.

FIG. 4 depicts one example of a flow diagram 400 for implementing aperception system in an autonomous vehicle. In FIG. 4, sensor data 434(e.g., generated by one or more sensors in sensor system 420) receivedby perception system 440 is depicted visually as sensor data 434 a-434 c(e.g., LIDAR data, color LIDAR data, 3D LIDAR data). At a stage 402 adetermination may be made as to whether or not the sensor data 434includes data representing a detected object. If a NO branch is taken,then flow diagram 400 may return to the stage 402 to continue analysisof sensor data 434 to detect object in the environment. If a YES branchis taken, then flow diagram 400 may continue to a stage 404 where adetermination may be made as to whether or not the data representing thedetected object includes data representing a traffic sign or light. If aYES branch is taken, then flow diagram 400 may transition to a stage 406where the data representing the detected object may be analyzed toclassify the type of light/sign object detected, such as a traffic light(e.g., red, yellow, and green) or a stop sign (e.g., based on shape,text and color), for example. Analysis at the stage 406 may includeaccessing a traffic object data store 424 where examples of datarepresenting traffic classifications may be compared with the datarepresenting the detected object to generate data representing a trafficclassification 407. The stage 406 may then transition to another stage,such as a stage 412. In some examples, the stage 404 may be optional,and the stage 402 may transition to a stage 408.

If a NO branch is taken from the stage 404, then flow diagram 400 maytransition to the stage 408 where the data representing the detectedobject may be analyzed to determine other object types to be classified.If a YES branch is taken, then flow diagram 400 may transition to astage 410 where the data representing the detected object may beanalyzed to classify the type of object. An object data store 426 may beaccessed to compare stored examples of data representing objectclassifications with the data representing the detected object togenerate data representing an object classification 411. The stage 410may then transition to another stage, such as a stage 412. If a NObranch is taken from the stage 408, then stage 408 may transition toanother stage, such as back to the stage 402.

At the stage 412, object data classified at the stages 406 and/or 410may be analyzed to determine if the sensor data 434 indicates motionassociated with the data representing the detected object. If motion isnot indicated, then a NO branch may be taken to a stage 414 where datarepresenting an object track for the detected object may be set tostatic (S). At a stage 416, data representing a location of the object(e.g., the static object) may be tracked. For example, a stationaryobject detected at time t0 may move at a later time t1 and become adynamic object. Moreover, the data representing the location of theobject may be included in data received by the planner system (e.g.,planner system 310 in FIG. 3B). The planner system may use the datarepresenting the location of the object to determine a coordinate of theobject (e.g., a coordinate relative to autonomous vehicle 100).

On the other hand, if motion is indicated in the detected object, a YESbranch may be taken to a stage 418 where data representing an objecttrack for the detected object may be set to dynamic (D). At a stage 419,data representing a location of the object (e.g., the dynamic object)may be tracked. The planner system may analyze the data representing theobject track and/or the data representing the location of the object todetermine if a detected object (static or dynamic) may potentially havea conflicting trajectory with respect to the autonomous vehicle and/orcome into too close a proximity of the autonomous vehicle, such that analert (e.g., from a light emitter and/or from an acoustic beam-steeringarray) may be used to alter a behavior of the object and/or the personcontrolling the object.

At a stage 422, one or more of the data representing the objectclassification, the data representing the object track, and the datarepresenting the location of the object, may be included with the objectdata 449 (e.g., the object data received by the planner system). As oneexample, sensor data 434 a may include data representing an object(e.g., a person riding a skateboard). Stage 402 may detect the object inthe sensor data 434 a. At the stage 404, it may be determined that thedetected object is not a traffic sign/light. The stage 408 may determinethat the detected object is of another class and may analyze at a stage410, based on data accessed from object data store 426, the datarepresenting the object to determine that the classification matches aperson riding a skateboard and output data representing the objectclassification 411. At the stage 412 a determination may be made thatthe detected object is in motion and at the stage 418 the object trackmay be set to dynamic (D) and the location of the object may be trackedat the stage 419 (e.g., by continuing to analyze the sensor data 434 afor changes in location of the detected object). At the stage 422, theobject data associated with sensor data 434 may include theclassification (e.g., a person riding a skateboard), the object track(e.g., the object is in motion), the location of the object (e.g., theskateboarder) in the environment external to the autonomous vehicle) andobject tracking data for example.

Similarly, for sensor data 434 b, flow diagram 400 may determine thatthe object classification is a pedestrian, the pedestrian is in motion(e.g., is walking) and has a dynamic object track, and may track thelocation of the object (e.g., the pedestrian) in the environment, forexample. Finally, for sensor data 434 c, flow diagram 400 may determinethat the object classification is a fire hydrant, the fire hydrant isnot moving and has a static object track, and may track the location ofthe fire hydrant. Note, that in some examples, the object data 449associated with sensor data 434 a, 434 b, and 434 c may be furtherprocessed by the planner system based on factors including but notlimited to object track, object classification and location of theobject, for example. As one example, in the case of the skateboarder andthe pedestrian, the object data 449 may be used for one or more oftrajectory calculation, threshold location estimation, motionprediction, location comparison, and object coordinates, in the eventthe planner system decides to implement an alert (e.g., by an exteriorsafety system) for the skateboarder and/or the pedestrian. However, theplanner system may decide to ignore the object data for the fire hydrantdue its static object track because the fire hydrant is not likely tohave a motion (e.g., it is stationary) that will conflict with theautonomous vehicle and/or because the fire hydrant is non-animate (e.g.,can't respond to or be aware of an alert, such as emitted light and/orbeam steered sound), generated by an exterior safety system of theautonomous vehicle), for example.

FIG. 5 depicts one example 500 of object prioritization by a plannersystem in an autonomous vehicle. Example 500 depicts a visualization ofan environment 590 external to the autonomous vehicle 100 as sensed by asensor system of the autonomous vehicle 100 (e.g., sensor system 320 ofFIG. 3B). Object data from a perception system of the autonomous vehicle100 may detect several objects in environment 590 including but notlimited to an automobile 581 d, a bicycle rider 583 d, a walkingpedestrian 585 d, and two parked automobiles 587 s and 589 s. In thisexample, a perception system may have assigned (e.g., based on sensordata 334 of FIG. 3B) dynamic “D” object tracks to objects 581 d, 583 dand 585 d, thus the label “d” is associated with the reference numeralsfor those objects. The perception system may also have assigned (e.g.,based on sensor data 334 of FIG. 3B) static “S” object tracks to objects587 s and 589 s, thus the label “s” is associated with the referencenumerals for those objects.

A localizer system of the autonomous vehicle may determine the localpose data 539 for a location of the autonomous vehicle 100 inenvironment 590 (e.g., X, Y, Z coordinates relative to a location onvehicle 100 or other metric or coordinate system). In some examples, thelocal pose data 539 may be associated with a center of mass (not shown)or other reference point of the autonomous vehicle 100. Furthermore,autonomous vehicle 100 may have a trajectory Tav as indicated by thearrow. The two parked automobiles 587 s and 589 s are static and have noindicated trajectory. Bicycle rider 583 d has a trajectory Tb that is ina direction approximately opposite that of the trajectory Tav, andautomobile 581 d has a trajectory Tmv that is approximately parallel toand in the same direction as the trajectory Tav. Pedestrian 585 d has atrajectory Tp that is predicted to intersect the trajectory Tav of thevehicle 100. Motion and/or position of the pedestrian 585 d inenvironment 590 or other objects in the environment 590 may be trackedor otherwise determined using metrics other than trajectory, includingbut not limited to object location, predicted object motion, objectcoordinates, predictive rate of motion relative to the location of theobject, and a predicted next location of the object, for example. Motionand/or position of the pedestrian 585 d in environment 590 or otherobjects in the environment 590 may be determined, at least in part, dueto probabilities. The probabilities may be based on data representingobject classification, object track, object location, and object type,for example. In some examples, the probabilities may be based onpreviously observed data for similar objects at a similar location.Further, the probabilities may be influenced as well by time of day orday of the week, or other temporal units, etc. As one example, theplanner system may learn that between about 3:00 pm and about 4:00 pm,on weekdays, pedestrians at a given intersection are 85% likely to crossa street in a particular direction.

The planner system may place a lower priority on tracking the locationof static objects 587 s and 589 s and dynamic object 583 d because thestatic objects 587 s and 589 s are positioned out of the way oftrajectory Tav (e.g., objects 587 s and 589 s are parked) and dynamicobject 583 d (e.g., an object identified as a bicyclist) is moving in adirection away from the autonomous vehicle 100; thereby, reducing oreliminating a possibility that trajectory Tb of object 583 d mayconflict with trajectory Tav of the autonomous vehicle 100.

However, the planner system may place a higher priority on tracking thelocation of pedestrian 585 d due to its potentially conflictingtrajectory Tp, and may place a slightly lower priority on tracking thelocation of automobile 581 d because its trajectory Tmv is not presentlyconflicting with trajectory Tav, but it may conflict at a later time(e.g., due to a lane change or other vehicle maneuver). Therefore, basedon example 500, pedestrian object 585 d may be a likely candidate for analert (e.g., using steered sound and/or emitted light) or other safetysystem of the autonomous vehicle 100, because the path of the pedestrianobject 585 d (e.g., based on its location and/or predicted motion) mayresult in a potential collision (e.g., at an estimated location 560)with the autonomous vehicle 100 or result in an unsafe distance betweenthe pedestrian object 585 d and the autonomous vehicle 100 (e.g., atsome future time and/or location). A priority placed by the plannersystem on tracking locations of objects may be determined, at least inpart, on a cost function of trajectory generation in the planner system.Objects that may be predicted to require a change in trajectory of theautonomous vehicle 100 (e.g., to avoid a collision or other extremeevent) may be factored into the cost function with greater significanceas compared to objects that are predicted to not require a change intrajectory of the autonomous vehicle 100, for example.

The planner system may predict one or more regions of probable locations565 of the object 585 d in environment 590 based on predicted motion ofthe object 585 d and/or predicted location of the object. The plannersystem may estimate one or more threshold locations (e.g., thresholdboundaries) within each region of probable locations 565. The thresholdlocation may be associated with one or more safety systems of theautonomous vehicle 100. The number, distance and positions of thethreshold locations may be different for different safety systems of theautonomous vehicle 100. A safety system of the autonomous vehicle 100may be activated at a parametric range in which a collision between anobject and the autonomous vehicle 100 is predicted. The parametric rangemay have a location within the region of probable locations (e.g.,within 565). The parametric range may be based in part on parameterssuch as a range of time and/or a range of distances. For example, arange of time and/or a range of distances in which a predicted collisionbetween the object 585 d and the autonomous vehicle 100 may occur. Insome examples, being a likely candidate for an alert by a safety systemof the autonomous vehicle 100 does not automatically result in an actualalert being issued (e.g., as determine by the planner system). In someexamples, am object may be a candidate when a threshold is met orsurpassed. In other examples, a safety system of the autonomous vehicle100 may issue multiple alerts to one or more objects in the environmentexternal to the autonomous vehicle 100. In yet other examples, theautonomous vehicle 100 may not issue an alert even though an object hasbeen determined to be a likely candidate for an alert (e.g., the plannersystem may have computed an alternative trajectory, a safe-stoptrajectory or a safe-stop maneuver that obviates the need to issue analert).

FIG. 6 depicts a top plan view of one example 600 of threshold locationsand associated escalating alerts in an active safety system in anautonomous vehicle. In FIG. 6, the example 500 of FIG. 5 is furtherillustrated in top plan view where trajectory Tav and Tp are estimatedto cross (e.g., based on location data for the vehicle 100 and thepedestrian object 585 d) at an estimated location denoted as 560.Pedestrian object 585 d is depicted in FIG. 6 based on pedestrian object585 d being a likely candidate for an alert (e.g., visual and/oracoustic) based on its predicted motion. The pedestrian object 585 d isdepicted having a location that is within the region of probablelocations 565 that was estimated by the planner system. Autonomousvehicle 100 may be configured to travel bi-directionally as denoted byarrow 680, that is, autonomous vehicle 100 may not have a front (e.g., ahood) or a back (e.g., a trunk) as in a conventional automobile. Notewhile FIG. 6 and other figures may describe implementations as appliedto bidirectional vehicles, the functions and/or structures need not beso limiting and may be applied to any vehicle, including unidirectionalvehicles. Accordingly, sensors of the sensor system and safety systemsmay be positioned on vehicle 100 to provide sensor and alert coverage inmore than one direction of travel of the vehicle 100 and/or to providesensor and alert coverage for objects that approach the vehicle 100 fromits sides 100 s (e.g., one or more sensors 328 in sensor system 330 inFIG. 3B may include overlapping regions of sensor coverage). The plannersystem may implement a safe-stop maneuver or a safe-stop trajectoryusing the bi-directional 680 travel capabilities to avoid a collisionwith an object, for example. As one example, the autonomous vehicle 100may be traveling in a first direction, come to a stop, and begin travelin a second direction that is opposite the first direction, and maymaneuver to a safe-stop location to avoid a collision or other extremeevent.

The planner system may estimate one or more threshold locations (e.g.,threshold boundaries, which may be functions of distances, etc.) in theenvironment 590, denoted as 601, 603 and 605, at which to issue an alertwhen the location of the object (e.g., pedestrian object 585 d)coincides with the threshold locations as denoted by points ofcoincidence 602, 604 and 606. Although three threshold locations aredepicted, there may be more or fewer than depicted. As a first example,as the trajectory Tp crosses the first threshold location 601 at a pointdenoted as 602, planner system may determine the location of thepedestrian object 585 d at the point 602 (e.g., having coordinates X1,Y1 of either a relative or an absolute reference frame) and thecoordinates of a location of the autonomous vehicle 100 (e.g., fromlocal pose data). The location data for the autonomous vehicle 100 andthe object (e.g., object 585 d) may be used to calculate a location(e.g., coordinates, an angle, a polar coordinate) for the direction ofpropagation (e.g., a main lobe or focus of the beam of steered acousticenergy may be aligned with the direction of propagation) of a beam ofsteered acoustic energy (e.g., an audio alert) emitted by an acousticbeam-steering array (e.g., one or more of a directional acoustic source,a phased array, a parametric array, a large radiator, a ultrasonicsource, etc.), may be used to determine which light emitter to activatefor a visual alert, may be used to determine which bladder(s) toactivate prior to a predicted collision with an object, and may be usedto activate other safety systems and/or the drive system of theautonomous vehicle 100, or any combination of the foregoing. As theautonomous vehicle 100 continues to travel in a direction 625 alongtrajectory Tav, from location L1 to location L2, the relative locationsof the pedestrian object 585 d and the autonomous vehicle 100 maychange, such that at the location L2, the object 585 d has coordinates(X2, Y2) at point 604 of the second threshold location 603. Similarly,continued travel in the direction 625 along trajectory Tav, fromlocation L2 to location L3, may change the relative locations of thepedestrian object 585 d and the autonomous vehicle 100, such that at thelocation L3, the object 585 d has coordinates (X3, Y3) at point 606 ofthe third threshold location 605.

As the distance between the autonomous vehicle 100 and the pedestrianobject 585 d decreases, the data representing the alert selected for asafety system may be different to convey an increasing sense of urgency(e.g., an escalating threat level) to the pedestrian object 585 d tochange or halt its trajectory Tp, or otherwise modify his/her behaviorto avoid a potential collision or close pass with the vehicle 100. Asone example, the data representing the alert selected for thresholdlocation 601, when the vehicle 100 may be at a relatively safe distancefrom the pedestrian object 585 d, may be a less alarming non-threateningalert a1 configured to garner the attention of the pedestrian object 585d in a non-threatening manner. As a second example, the datarepresenting the alert selected for threshold location 603, when thevehicle 100 may be at a cautious distance from the pedestrian object 585d, may be a more aggressive urgent alert a2 configured to gain theattention of the pedestrian object 585 d in a more urgent manner. As athird example, the data representing the alert selected for thresholdlocation 605, when the vehicle 100 may be at a potentially un-safedistance from the pedestrian object 585 d, may be a very aggressiveextremely urgent alert a3 configured to gain the attention of thepedestrian object 585 d in an extremely urgent manner. As the distancebetween the autonomous vehicle 100 and the pedestrian object 585 ddecreases, the data representing the alerts may be configured to conveyan increasing escalation of urgency to pedestrian object 585 d (e.g.,escalating acoustic and/or visual alerts to gain the attention ofpedestrian object 585 d). Estimation of positions of the thresholdlocations in the environment 590 may be determined by the planner systemto provide adequate time (e.g., approximately 5 seconds or more), basedon a velocity of the autonomous vehicle, before the vehicle 100 arrivesat a predicted impact point 560 with the pedestrian object 585 d (e.g.,a point 560 in environment 590 where trajectories Tav and Tp areestimated to intersect each other). Point 560 may change as the speedand/or location of the object 585 d, the vehicle 100, or both changes.In some examples, in concert with implementing active alerts (e.g.,acoustic and/or visual alerts), the planner system of the autonomousvehicle 100 may be configured to actively attempt to avoid potentialcollisions (e.g., by calculating alternative collision avoidancetrajectories and executing one or more of those trajectories) bycalculating (e.g., continuously) safe trajectories (e.g., when possiblebased on context), while simultaneously, issuing alerts as necessary toobjects in the environment when there is a meaningful probability theobjects may collide or otherwise pose a danger to the safety ofpassengers in the autonomous vehicle 100, to the object, or both.

In FIG. 6, trajectory Tp of the pedestrian object 585 d need not be astraight line as depicted and the trajectory (e.g., the actualtrajectory) may be an arcuate trajectory as depicted in example 650 ormay be a non-linear trajectory as depicted in example 670. Points 652,654 and 656 in example 650 and points 672, 674 and 676 in example 670,depict points where the trajectory Tp intersects threshold locations651, 653 and 655 and threshold locations 671, 673 and 675, respectively.The planner system may process object data from the perception systemand local pose data from the localizer system to calculate the thresholdlocation(s). The location, shape (e.g., linear, arcuate, non-linear),orientation (e.g., with respect to the autonomous vehicle and/or object)and other characteristics of the threshold location may be applicationdependent and is not limited to the examples depicted herein. Forexample, in FIG. 6, threshold locations (601, 603 and 605) in example600 are aligned approximately perpendicular to the trajectory Tp ofpedestrian object 585 d; however, other configurations and orientationsmay be calculated and implemented by the planner system (e.g., seeexamples 650 and 670). As another example, threshold locations may bealigned approximately perpendicular (or other orientation) to thetrajectory Tav of the autonomous vehicle 100. Furthermore, thetrajectory of the object may be analyzed by the planner system todetermine the configuration of the threshold location(s). As oneexample, if the object is on a trajectory that is parallel to thetrajectory of the autonomous vehicle 100, then the threshold location(s)may have an arcuate profile and may be aligned with the trajectory ofthe object.

FIG. 7 depicts one example of a flow diagram 700 for implementing aplanner system in an autonomous vehicle. In FIG. 7, planner system 710may be in communication with a perception system 740 from which itreceives object data 749, and a localizer system 730 from which itreceives local pose data 739. Object data 749 may include dataassociated with one or more detected objects. For example, in a typicalscenario, object data 749 may include data associated with a largenumber of detected objects in the environment external to the autonomousvehicle 100. However, some detected objects need not be tracked or maybe assigned a lower priority based on the type of object. For example,fire hydrant object 434 c in FIG. 4 may be a static object (e.g., fixedto the ground) and may not require processing for an alert or activationof other safety systems; whereas, skateboarder object 434 a may requireprocessing for an alert (e.g., using emitted light, steered sound orboth) due to it being a dynamic object and other factors, such aspredicted human behavior of skateboard riders, a location of theskateboarder object 434 a in the environment that may indicate thelocation (e.g., a predicted location) may conflict with a trajectory ofthe autonomous vehicle 100, for example.

Further to FIG. 7, an example of three detected objects is denoted as734 a-734 c. There may be object data 749 for more or fewer detectedobjects as denoted by 734. The object data for each detected object mayinclude but is not limited to data representing: object location 721;object classification 723; and object track 725. Planner system 710 maybe configured to implement object type determination 731. Object typedetermination 731 may be configured to receive the data representingobject classification 723, the data representing the object track 725and data representing object types that may be accessed from an objecttypes data store 724. The object type determination 731 may beconfigured to compare the data representing the object classification723 and the data representing the object track 725 with datarepresenting object types (e.g., accessed from data store 724) todetermine data representing an object type 733. Examples of datarepresenting an object type 733 may include but are not limited to astatic grounded object type (e.g., the fire hydrant 434 c of FIG. 4) anda dynamic pedestrian object (e.g., pedestrian object 585 d of FIG. 5).

Object dynamics determination 735 may be configured to receive the datarepresenting the object type 733 and the data representing the objectlocation 721. Object dynamics determination 735 may be furtherconfigured to access an object dynamics data store 726. Object dynamicsdata store 726 may include data representing object dynamics. Objectdynamics determination 735 may be configured to compare datarepresenting object dynamics with the data representing the object type733 and the data representing the object location 721 to determine datarepresenting a predicted object motion 737.

Object location predictor 741 may be configured to receive the datarepresenting the predicted object motion 737, the data representing thelocation of the autonomous vehicle 743 (e.g., from local pose data 739),the data representing the object location 721 and the data representingthe object track 725. The object location predictor 741 may beconfigured to process the received data to generate data representing apredicted object location 745 in the environment. Object locationpredictor 741 may be configured to generate data representing more thanone predicted object location 745. The planner system 710 may generatedata representing regions of probable locations (e.g., 565 in FIGS. 5and 6) of an object.

Threshold location estimator 747 may be configured to receive the datarepresenting the location of the autonomous vehicle 743 (e.g., fromlocal pose data 739) and the data representing the predicted objectlocation 745 and generate data representing one or more thresholdlocations 750 in the environment associated with an alert to betriggered (e.g., a visual alert and/or an acoustic alert), or associatedwith activation of one or more safety systems of vehicle 100, forexample. The one or more threshold locations 750 may be located with theregions of probable locations (e.g., 601, 603 and 605 within 565 in FIG.6).

FIG. 8 depicts one example 800 of a block diagram of systems in anautonomous vehicle. In FIG. 8, the autonomous vehicle 100 may include asuite of sensors 820 positioned at one or more locations on theautonomous vehicle 100. Each suite 820 may have sensors including butnot limited to LIDAR 821 (e.g., color LIDAR, three-dimensional colorLIDAR, two-dimensional LIDAR, etc.), an image capture device 823 (e.g.,a digital camera), RADAR 825, a microphone 827 (e.g., to capture ambientsound), and a loudspeaker 829 (e.g., to greet/communicate withpassengers of the AV 100). Microphones 871 (e.g., to may be configuredto capture sound from drive system components such as propulsion systemsand/or braking systems) and may be positioned at suitable locationsproximate to drive system components to detect sound generated by thosecomponents, for example. Each suite of sensors 820 may include more thanone of the same types of sensor, such as two image capture devices 823,or microphone 871 positioned proximate to each wheel 852, for example.Microphone 871 may be configured to capture audio signals indicative ofdrive operations of the autonomous vehicle 100. Microphone 827 may beconfigured to capture audio signals indicative of ambient sounds in theenvironment external to the autonomous vehicle 100. Multiple microphones827 may be paired or otherwise grouped to generate signals that may beprocessed to estimate sound source location of sounds originating in theenvironment 890, for example. Autonomous vehicle 100 may include sensorsfor generating data representing location of the autonomous vehicle 100,and those sensors may include but are not limited to a globalpositioning system (GPS) 839 a and/or an inertial measurement unit (IMU)839 b. Autonomous vehicle 100 may include one or more sensors ENV 877for sensing environmental conditions in the environment external to theautonomous vehicle 100, such as air temperature, air pressure, humidity,barometric pressure, etc. Data generated by sensor(s) ENV 877 may beused to calculate the speed of sound in processing of data used byarray(s) 102 (see arrays 102 depicted in FIG. 9), such as wave frontpropagation times, for example. Autonomous vehicle 100 may include oneor more sensors MOT 888 configured to detect motion of the vehicle 100(e.g., motion due to an impact from a collision, motion due to anemergency/evasive maneuvering, etc.). As one example, sensor MOT 888 mayinclude but is not limited to an accelerometer, a multi-axisaccelerometer and a gyroscope. Autonomous vehicle 100 may include one ormore rotation sensors (not shown) associated with wheels 852 andconfigured to detect rotation 853 of the wheel 852 (e.g., a wheelencoder). For example, each wheel 852 may have an associated wheelencoder configured to detect rotation of the wheel 852 (e.g., an axle ofthe wheel 852 and/or a propulsion component of the wheel 852, such as anelectric motor, etc.). Data representing a rotation signal generated bythe rotation sensor may be received by one or more systems of theautonomous vehicle 100, such as the planner system, the localizersystem, the perception system, the drive system, and one or more of thesafety systems, for example.

A communications network 815 may route signals and/or data to/fromsensors, one or more safety systems 875 (e.g., a bladder, a seatactuator, a seat belt tensioner), and other components of the autonomousvehicle 100, such as one or more processors 810 and one or more routers830, for example. Routers 830 may route signals and/or data from:sensors in sensors suites 820, one or more acoustic beam-steering arrays102 (e.g., one or more of a directional acoustic source, a phased array,a parametric array, a large radiator, of ultrasonic source), one or morelight emitters, between other routers 830, between processors 810, driveoperation systems such as propulsion (e.g., electric motors 851),steering, braking, one or more safety systems 875, etc., and acommunications system 880 (e.g., for wireless communication withexternal systems and/or resources).

In FIG. 8, one or more microphones 827 may be configured to captureambient sound in the environment 890 external to the autonomous vehicle100. Signals and/or data from microphones 827 may be used to adjust gainvalues for one or more speakers positioned in one or more of theacoustic beam-steering arrays (not shown). As one example, loud ambientnoise, such as emanating from a construction site may mask or otherwiseimpair audibility of the beam of steered acoustic energy 104 beingemitted by an acoustic beam-steering array. Accordingly, the gain may beincreased or decreased based on ambient sound (e.g., in dB or othermetric such as frequency content).

Microphones 871 may be positioned in proximity of drive systemcomponents, such as electric motors 851, wheels 852, or brakes (notshown) to capture sound generated by those systems, such as noise fromrotation 853, regenerative braking noise, tire noise, and electric motornoise, for example. Signals and/or data generated by microphones 871 maybe used as the data representing the audio signal associated with anaudio alert, for example. In other examples, signals and/or datagenerated by microphones 871 may be used to modulate the datarepresenting the audio signal. As one example, the data representing theaudio signal may constitute an audio recording (e.g., a digital audiofile) and the signals and/or data generated by microphones 871 may beused to modulate the data representing the audio signal. Further to theexample, the signals and/or data generated by microphones 871 may beindicative of a velocity of the vehicle 100 and as the vehicle 100 slowsto a stop at a pedestrian cross-walk, the data representing the audiosignal may be modulated by changes in the signals and/or data generatedby microphones 871 as the velocity of the vehicle 100 changes. Inanother example, the signals and/or data generated by microphones 871that are indicative of the velocity of the vehicle 100 may be used asthe data representing the audio signal and as the velocity of thevehicle 100 changes, the sound being emitted by the acousticbeam-steering array 102 may be indicative of the change in velocity ofthe vehicle 100. As such, the sound (or acoustic energy magnitude) maybe changed (e.g., in volume, frequency, etc.) based on velocity changes.The above examples may be implemented to audibly notify pedestrians thatthe autonomous vehicle 100 has detected their presence at the cross-walkand is slowing to a stop. In yet other examples, signals and/or datagenerated by a system of the autonomous vehicle 100, such as datarepresenting a velocity of the vehicle 100, may be used to modulate thedata representing the audio signal. The data representing the audiosignal may constitute an audio recording (e.g., a digital audio file)and the signals and/or data generated by microphones 871 or by localpose data (e.g., from the localizer system) may be used to modulate thedata representing the audio signal. Further to the example, as thevehicle 100 slows to a stop at a pedestrian cross-walk, the datarepresenting the audio signal may be modulated as the velocity of thevehicle 100 changes.

One or more processors 810 may be used to implement one or more of theplanner system, the localizer system, the perception system, one or moresafety systems, and other systems of the vehicle 100, for example. Oneor more processors 810 may be configured to execute algorithms embodiedin a non-transitory computer readable medium, to implement one or moreof the planner system, the localizer system, the perception system, oneor more safety systems, or other systems of the vehicle 100, forexample. The one or more processors 810 may include but are not limitedto circuitry, logic, field programmable gate array (FPGA), applicationspecific integrated circuits (ASIC), programmable logic, a digitalsignal processor (DSP), a graphics processing unit (GPU), amicroprocessor, a microcontroller, a big fat computer (BFC) or others,or clusters thereof.

FIG. 9 depicts a top view of one example 900 of an acousticbeam-steering array in an exterior safety system of an autonomousvehicle. In FIG. 9, sensor suites (e.g., 820 in FIG. 8) may bepositioned at corner portions of the autonomous vehicle 100 (e.g., apillar section) and enclosures for acoustic beam-steering arrays 102 maybe positioned on an upper surface 100 u (e.g., on a roof or otherlocations on the vehicle) of the autonomous vehicle 100 and may bepositioned to direct their respective beams of steered acoustic energy104 (e.g., see FIG. 11) outward into the environment towards a locationof an object targeted to receive an acoustic alert. Acousticbeam-steering arrays 102 (e.g., a directional acoustic source, a phasedarray, a parametric array, a large radiator, of ultrasonic source) maybe configured to provide coverage of steered acoustic energy at one ormore objects disposed in the environment external to the autonomousvehicle 100 and the coverage may be in an arc of about 360 degreesaround the autonomous vehicle 100, for example. The acousticbeam-steering arrays 102 need not be the same size, shape, or have thesame number of speakers. The acoustic beam-steering arrays 102 need notbe linear as depicted in FIG. 9. Further to FIG. 9, acousticbeam-steering arrays 102 denoted as C and D have different dimensionsthan acoustic beam-steering arrays 102 denoted as A and B. Sensor suites820 may be positioned to provide sensor coverage of the environmentexternal to the autonomous vehicle 100 for one acoustic beam-steeringarray 102 or multiple acoustic beam-steering arrays 102. In the case ofmultiple acoustic beam-steering arrays 102, the sensor suites 820 mayprovide overlapping regions of sensor coverage. A perception system ofthe autonomous vehicle 100 may receive sensor data from more than onesensor or suite of sensors to provide data for the planning system toimplement activation of one or more of the arrays 102 to generate anacoustic alert and/or to activate one or more other safety systems ofvehicle 100, for example. The autonomous vehicle 100 may not have afront (e.g., a hood) or a rear (e.g., a trunk) and therefore may beconfigured for driving operations in at least two different directionsas denoted by arrow 980. Accordingly, the acoustic beam-steering arrays102 may not have a front or rear designation and depending on whichdirection the autonomous vehicle 100 is driving in, array 102 (A) may bethe array facing the direction of travel or array 102 (B) may be thearray facing the direction of travel.

Other safety systems of the autonomous vehicle 100 may be disposed atinterior 100 i (shown in dashed line) and exterior 100 e locations onthe autonomous vehicle 100 and may be activated for generating alerts orother safety functions by the planner system using the sensor data fromthe sensor suites. The overlapping regions of sensor coverage may beused by the planner system to activate one or more safety systems inresponse to multiple objects in the environment that may be positionedat locations around the autonomous vehicle 100.

FIGS. 10A-10B depicts top plan views of examples of sensor coverage. Inexample 1010 of FIG. 10A, one of the four sensor suites 820 (denoted inunderline) may provide sensor coverage 1011, using one or more of itsrespective sensors, of the environment 1090 in a coverage region thatmay be configured to provide sensor data for one or more systems, suchas a perception system, localizer system, planner system and safetysystems of the autonomous vehicle 100, for example. In the top plan viewof FIG. 10A, arrows A, B, C and D demarcate four quadrants 1-4 thatsurround the autonomous vehicle 100. In example 1010, sensor coverage1011 by a single suite 820 may be partial sensor coverage because theremay be partial senor blind spots not covered by the single suite 820 inquadrants 1, 3 and 4, and full sensor coverage in quadrant 2.

In example 1020, a second of the four sensor suites 820 may providesensor coverage 1021 that overlaps with sensor coverage 1011, such thatthere may be partial sensor coverage in quadrants 1 and 4 and fullsensor coverage in quadrants 2 and 3. In FIG. 10B, in example 1030, athird of the four sensor suites 820 may provide sensor coverage 1031that overlaps with sensor coverage 1011 and 1021, such that quadrants 2,3 and 4 have full sensor coverage and quadrant 1 has partial coverage.Finally, in example 1040, a fourth of the four sensor suites 820 (e.g.,all four sensor suites are on-line) may provide sensor coverage 1041that overlaps with sensor coverage 1011, 1021 and 1031, such thatquadrants 1-4 have full coverage. The overlapping sensor regions ofcoverage may allow for redundancy in sensor coverage if one or moresensor suites 820 and/or one or more of their respective sensors (e.g.,LIDAR, Camera, RADAR, etc.) are damaged, malfunction, or are otherwiserendered inoperative. One or more of the sensor coverage patterns 1011,1021, 1031 and 1041 may allow the planner system to implement activationof one or more safety systems based on the position of those safetysystems on the autonomous vehicle 100. As one example, bladders of abladder safety system positioned on a side of the autonomous vehicle 100(e.g., the side of the vehicle 100 with arrow C) may be activated tocounter a collision from an object approaching the vehicle 100 from theside.

FIG. 11A depicts one example of an acoustic beam-steering array in anexterior safety system of an autonomous vehicle. In FIG. 11A, controldata 317 from planner system 310 may be communicated to vehiclecontroller 350 which may in turn communicate exterior data 325 beingconfigured to implement an acoustic alert by acoustic beam-steeringarray 102. Although one array 102 is depicted, the autonomous vehicle100 may include more than one array 102 as denoted by 1103 (e.g., seearrays 102 in FIG. 9). Exterior data 325 received by array 102 mayinclude but is not limited to object location data 1148 (e.g., acoordinate of object 1134 in the environment 1190), audio signal data1162, trigger signal data 1171 and optionally, modulation signal data1169.

Acoustic beam-steering array 102 may include a processor 1105 (e.g., adigital signal processor (DSP), field programmable gate array (FPGA),central processing unit (CPU), microprocessor, micro-controller, CPU'sand/or clusters thereof, or other embedded processing system) thatreceives the exterior data 325 and processes the exterior data 325 togenerate the beam 104 of steered acoustic energy (e.g., at angle βrelative to a trajectory T_(AV) of AV 100) into the environment 1190(e.g., in response to receiving the data representing the trigger signal1171). Acoustic beam-steering array 102 may include several speakers S,with each speaker S in the array 102 being coupled with an output ofamplifier A. Each amplifier A may include a gain input and a signalinput. Processor 1105 may calculate data representing a signal gain Gfor the gain input of each amplifier A and may calculate datarepresenting a signal delay D for the signal input of each amplifier A.Processor 1105 may access and/or or receive data representinginformation on speakers S (e.g., from an internal and/or external datasource) and the information may include but is not limited to an arraywidth (e.g., a distance between the first speaker and last speaker inarray 102), speaker S spacing in the array (e.g., a distance betweenadjacent speakers S in array 102), a wave front distance betweenadjacent speakers S in the array, the number of speakers S in the array,speaker characteristics (e.g., frequency response, output level per wattof power, radiating area, etc.), just to name a few. Each speaker S andits associated amplifier A in array 102 may constitute a monauralchannel and the array 102 may include n monaural channels, where thenumber of monaural channels n, depending on speaker size and anenclosure size of the array 102, may vary. For example n may be 30 ormay be 320. For example, in an ultrasonic parametric arrayimplementation of the array 102, n may be on the order of about 80 toabout 300. In some examples, the spacing between speakers in the array102 may not be linear for some or all of the speakers in the array 102.

In example 1100 of FIG. 11A, the steered beam of acoustic energy 104 isbeing steered at an object 1134 (e.g., a skateboarder) having apredicted location Lo in the environment 1190 that may conflict with thetrajectory Tav of the autonomous vehicle 100. A location of the object1134 may be included in the exterior data 325 as the object locationdata 1148. Object location data 1148 may be data representing acoordinate of the object 1134 (e.g., an angle, Cartesian coordinates, apolar coordinate, etc.). Array 102 may process the object location data1148 to calculate an angle β to direct the beam 104 at the object 1134.The angle β may be measured relative to the trajectory Tav or relativeto some point on a frame of the vehicle 100 (e.g., see 100 r in FIG.11C). If the predicted location Lo changes due to motion of the object1134, then the other systems of the autonomous vehicle system 101 maycontinue to track and update data associated with the autonomous vehicle100 and the object 1134 to compute updated object location data 1134.Accordingly, the angle β may change (e.g., is re-computed by processor1105) as the location of the object 1134 and/or the vehicle 100 changes.In some examples, the angle β may be computed (e.g., by the plannersystem) to take into account not only the currently predicted direction(e.g., coordinate) to the object, but also a predicted direction to theobject at a future time t, where a magnitude of a time delay in firingthe array 102 may be a function of a data processing latency of aprocessing system of the vehicle 100 (e.g., processing latency in one ormore of the planner system, the perception system or the localizersystem). Accordingly, the planner system may cause the array 102 to emitthe sound where the object is predicted to be a some fraction of asecond from the time the array 102 is fired to account for theprocessing latency, and/or the speed of sound itself (e.g., based onenvironmental conditions), depending on how far away the object is fromthe vehicle 100, for example.

FIG. 11B depicts one example of a flow diagram 1150 for implementing anacoustic beam-steering in an autonomous vehicle. At a stage 1152, datarepresenting a trajectory of the autonomous vehicle 100 in theenvironment may be calculated based on data representing a location ofthe autonomous vehicle 100 (e.g., local pose data). At a stage 1154,data representing a location (e.g., a coordinate) of an object disposedin the environment may be determined (e.g., from object track dataderived from sensor data). Data representing an object type may beassociated with the data representing the location of the object. At astage 1156, data representing a predicted location of the object in theenvironment may be predicted based on the data representing the objecttype and the data representing the location of the object in theenvironment. At a stage 1158, data representing a threshold location inthe environment associated with an audio alert (e.g., from array 102)may be estimated based on the data representing the predicted locationof the object and the data representing the trajectory of the autonomousvehicle. At a stage 1160, data representing an audio signal associatedwith the audio alert may be selected. At a stage 1162, the location ofthe object being coincident with the threshold location may be detected.As one example, the predicted location of the object crossing thethreshold location may be one indication of coincidence. At a stage1164, data representing a signal gain “G” associated with a speakerchannel of the acoustic beam-steering array 102 may be calculated. Anarray 102 having n speaker channels may have n different gains “G”calculated for each speaker channel in the n speaker channels. The datarepresenting the signal gain “G” may be applied to a gain input of anamplifier A coupled with a speaker S in a channel (e.g., as depicted inarray 102 of FIG. 11A). At a stage 1166, data representing a signaldelay “D” may be calculated for each of the n speaker channels of thearray 102. The data representing the signal delay “D” may be applied toa signal input of an amplifier A coupled with a speaker S in a channel(e.g., as depicted in array 102 of FIG. 11A). At a stage 1168, thevehicle controller (e.g., 350 of FIG. 11A) may implement the acousticalert by causing the array 102 (e.g., triggering, activating, orcommanding array 102) to emit a beam of steered acoustic energy (e.g.,beam 104) in a direction of propagation (e.g., direction of propagation106) determined by the location of the object (e.g., a coordinate of theobject in the environment), the beam of steered acoustic energy beingindicative of the audio signal.

The stages of flow diagram 1150 may be implemented for one or more ofthe arrays 102, and one or more stages of flow diagram 1150 may berepeated. For example, predicted object path, object location (e.g.,object coordinates), predicted object location, threshold location,vehicle trajectory, audio signal selection, coincidence detection, andother stages may be repeated to update and/or process data as necessarywhile the autonomous vehicle 100 travels through the environment and/oras the object changes locations in the environment.

FIG. 11C depicts a top plan view of one example 1170 of an autonomousvehicle steering acoustic energy associated with an acoustic alert to anobject. In FIG. 11C, autonomous vehicle 100 may have a trajectory Tavalong a roadway between lane markers denoted by dashed lines 1177. Adetected object 1171 in the environment external to the autonomousvehicle 100 has been classified as an automotive object type having apredicted location Lc in the environment that is estimated to conflictwith the trajectory Tav of the autonomous vehicle 100. Planner systemmay generate three threshold locations t-1, t-2, and t-3 (e.g., havingarcuate profiles 1121, 1122 and 1123), respectively. The three thresholdlocations t-1, t-2, and t-3 may be representative of escalating threatlevels ranked from a low threat level at threshold location t-1 (e.g., arelatively safe distance away from vehicle 100), a medium threat levelat threshold location t-2 (e.g., a distance that is cautiously close tothe vehicle 100) and a high threat level at threshold location t-3(e.g., a distance that is dangerously close to vehicle 100), forexample. A different audio signal for an audio alert to be generated ateach of the three threshold locations t-1, t-2, and t-3 may be selectedto audibly convey the escalating levels of threat as the predictedlocation Lc of the object 1170 brings the object 1171 closer to thelocation of the autonomous vehicle 100 (e.g., close enough for apotential collision with autonomous vehicle 100). The beam of steeredacoustic energy 104 may be indicative of the information included in theselected audio signal (e.g., audio signals 104 a, 104 b and 104 c).

When object type 1171 crosses or otherwise has its location coincidentwith threshold location t-1, the planner system may generate a triggersignal to activate the acoustic array 102 positioned to generate anacoustic alert using an audio signal 104 a (e.g., to convey anon-threatening acoustic alert) along a direction of propagation 106 abased on a coordinate of the object 1171. For example, the coordinatemay be an angle βa measured between the trajectory Tav and the directionof propagation 106 a. A reference point for the coordinate (e.g., anglesβa, βb and βc) may be a point 102 r on the array 102 or some otherlocation on the autonomous vehicle 102, such as a point 100 r, forexample. As the object 1171 continues along its predicted location Lcand crosses threshold location t-2, another acoustic alert may betriggered by the planner system, using coordinate βb, an audio signal104 b (e.g., to convey an urgent acoustic alert) and a direction ofpropagation 106 b. Further travel by object 1171 that crosses thresholdlocation t-3 may trigger yet another acoustic alert by planner systemusing a coordinate βc, an audio signal 104 c (e.g., to convey anextremely urgent acoustic alert) and direction of propagation 106 c. Inthis example, a different audio signal (e.g., a digital audio file,whether pre-recorded or dynamically generated, having different acousticpatterns, different magnitudes of acoustic power and/or volume, etc.)may be selected for audio signals 104 a, 104 b and 104 c to convey theincreasing levels of escalation to the object 1171 (e.g., toacoustically alert a driver of the vehicle).

For each of the acoustic alerts triggered by the planner system, thepredicted location Lc of the object 1171 may change (e.g., relative tothe location of the vehicle 100) and the planner system may receiveupdated object data (e.g., object tracking data from the perceptionsystem) to calculate (e.g., in real-time) changes in the location of theobject 1171 (e.g., to calculate or recalculate βa, βb and βc). The audiosignal selected by planner system for each threshold location t-1, t-2and t-3 may be different and may be configured to include audibleinformation intended to convey ever increasing degrees of urgency forthreshold locations t-1 to t-2 and t-2 to t-3, for example. The audiosignal(s) selected by the planner system may be configured, based on theobject type data for object 1171, to acoustically penetrate structures(1173, 1179) of the automobile, such as auto glass, door panels, etc.,in order to garner the attention of a driver of the automobile. In someexamples, if the object (e.g., the driver of the automobile) is detected(e.g., by the planner system) as changing its behavior (e.g., changingits predicted location, its predicted object path, or otherwise is nolonger a threat to the vehicle 100 and/or its passengers), then theplanner system may cause the array 102 to de-escalate the acoustic alertby lowering the level of urgency of the alert (e.g., by selecting anaudio signal indicative of the lowered level of urgency). As oneexample, the selected audio signal may be configured to generatefrequencies in a range from about 220 Hz to about 450 Hz to acousticallypenetrate structures (1173, 1179) on the object 1171. As anotherexample, the array 102 may be configured to generate sound atfrequencies in a range from about 220 Hz to about 4.5 kHz, or any otherfrequency range. Further, the sound generated by the array 102 may bechanged (e.g., in volume, frequency, etc.) based on velocity changes inthe autonomous vehicle 100. Sound frequencies emitted by the array 102are not limited to the foregoing examples, and the array 102 may beconfigured to generate sound at frequencies that are within the range ofhuman hearing, above the range of human hearing (e.g., ultrasonicfrequencies), below the range of human hearing (e.g., infrasonicfrequencies), or some combination thereof.

Autonomous vehicle 100 is depicted as having two arrays 102 disposed onan upper surface 100 u (e.g., a roof of the vehicle 100); however, thevehicle 100 may have more or fewer arrays 102 than depicted and theplacement of the arrays 102 may be different than depicted in FIG. 11C.Acoustic beam-steering array 102 may include several speakers and theirassociated amplifiers and driving electronics (e.g., a processor, a DSP,etc.). For purposes of explanation, each amplifier/speaker pair will bedenoted as a channel, such that array 102 will include n-channelsdenoted as C1 for channel one all the way to Cn for the nth channel. Inan enlarged view of the acoustic beam-steering array 102, each speaker Smay be spaced apart from an adjacent speaker by a distance d. Distance d(e.g., a spacing between adjacent speakers (S) may be the same for allspeakers S in the array 102, such that all of the speakers S are spacedapart from one another by the distance d. In some examples, distance dmay vary among the speakers S in the array 102 (e.g., distance d neednot be identical between adjacent speakers in array 102). Distance d maybe measured from a reference point on each speaker S, such as a centerpoint of each speaker S.

A width W of the array 102 may be measured as a distance between thefirst speaker in the array 102 (e.g., channel C1) to the last speaker inthe array 102 (e.g., channel Cn) and the width W may be measured fromthe center of the speaker in C1 to the center of the speaker in Cn, forexample. In the direction of propagation 106 of the acoustic waves 104generated by array 102, wave-fronts launched by adjacent speakers S inthe array 102 may be delayed in time by a wave-front propagation timet_(d). The wave-front front propagation time t_(d) may be calculated asa distance between adjacent wave-fronts r multiplied by the speed ofsound c (e.g., t_(d)=r*c). In examples where the distance d betweenspeakers S is the same for all speakers S in the array 102, the delay Dcalculated for each speaker S may be an increasing integer multiple oft_(d). Therefore, for channel C1: (t_(d1)=(r*c)*1), for channel C2:(t_(d2)=(r*c)*2), and for channel Cn: (t_(dn)=(r*c)*n), for example. Insome examples, the speed of sound c may be calculated using data from anenvironmental sensor (e.g., sensor 877 of FIG. 8) to more accuratelydetermine a value for the speed of sound c based on environmentalconditions, such as altitude, air pressure, air temperature, humidity,and barometric pressure, for example.

FIG. 12A depicts one example 1200 of light emitters positioned externalto an autonomous vehicle. In FIG. 12A, control data 317 from plannersystem 310 may be communicated to vehicle controller 350 which may inturn communicate exterior data 325 being configured to implement avisual alert using a light emitter 1202. Although one light emitter 1202is depicted, the autonomous vehicle 100 may include more than one lightemitter 1202 as denoted by 1203. Exterior data 325 received by lightemitter 1202 may include but is not limited to data representing a lightpattern 1212, data representing a trigger signal 1214 being configuredto activate one or more light emitters 1202, data representing an arrayselect 1216 being configured to select which light emitters 1202 of thevehicle 100 to activate (e.g., based on an orientation of the vehicle100 relative to the object the visual alert is targeted at), andoptionally, data representing drive signaling 1218 being configured todeactivate light emitter operation when the vehicle 100 is using itssignaling lights (e.g., turn signal, brake signal, etc.), for example.In other examples, one or more light emitters 1202 may serve as a signallight, a head light, or both. An orientation of the autonomous vehicle100 relative to an object may be determined based on a location of theautonomous vehicle 100 (e.g., from the localizer system) and a locationof the object (e.g., from the perception system), a trajectory of theautonomous vehicle 100 (e.g., from the planner system) and a location ofthe object, or both, for example.

The light emitter 1202 may include a processor 1205 being configured toimplement visual alerts based on the exterior data 325. A selectfunction of the processor 1205 may receive the data representing thearray select 1216 and enable activation of a selected light emitter1202. Selected light emitters 1202 may be configured to not emit light Luntil the data representing the trigger signal 1214 is received by theselected light emitter 1202. The data representing the light pattern1212 may be decoder by a decode function, and sub-functions may operateon the decoded light pattern data to implement a color function beingconfigured to determine a color of light to be emitted by light emitter1202, an intensity function being configured to determine an intensityof light to be emitted by light emitter 1202, and a duration functionbeing configured to determine a duration of light emission from thelight emitter 1202, for example. A data store (Data) may include datarepresenting configurations of each light emitter 1202 (e.g., number oflight emitting elements E, electrical characteristics of light emittingelements E, positions of light emitters 1202 on vehicle 100, etc.).Outputs from the various functions (e.g., decoder, select, color,intensity and duration) may be coupled with a driver 1207 configured toapply signals to light emitting elements E1-En of the light emitter1202. Each light emitting element E may be individually addressablebased on the data representing the light pattern 1212. Each lightemitter 1202 may include several light emitting elements E, such that nmay represent the number of light emitting elements E in the lightemitter 1202. As one example, n may be greater than 50. The lightemitters 1202 may vary in size, shape, number of light emitting elementsE, types of light emitting elements E, and locations of light emitters1202 positioned external to the vehicle 100 (e.g., light emitters 1202coupled with a structure of the vehicle 100 operative to allow the lightemitter to emit light L into the environment, such as roof 100 u orother structure of the vehicle 100).

As one example, light emitting elements E1-En may be solid-state lightemitting devices, such as light emitting diodes or organic lightemitting diodes, for example. The light emitting elements E1-En may emita single wavelength of light or multiple wavelengths of light. The lightemitting elements E1-En may be configured to emit multiple colors oflight, based on the data representing the light pattern 1212, such asred, green, blue and one or more combinations of those colors, forexample. The light emitting elements E1-En may be a RGB light emittingdiode (RGB LED) as depicted in FIG. 12A. The driver 1207 may beconfigured to sink or source current to one or more inputs of the lightemitting elements E1-En, such as one or more of the red-R, green-G orblue-B inputs of the light emitting elements E1-En to emit light Lhaving a color, intensity, and duty cycle based on the data representingthe light pattern 1212. Light emitting elements E1-En are not limited tothe example 1200 of FIG. 12A, and other types of light emitter elementsmay be used to implement light emitter 1202.

Further to FIG. 12A, an object 1234 having a predicted location Lo thatis determined to be in conflict with a trajectory Tav of the autonomousvehicle 100 is target for a visual alert (e.g., based on one or morethreshold locations in environment 1290). One or more of the lightemitters 1202 may be triggered to cause the light emitter(s) to emitlight L as a visual alert. If the planner system 310 determines that theobject 1234 is not responding (e.g., object 1234 has not altered itslocation to avoid a collision), the planner system 310 may selectanother light pattern configured to escalate the urgency of the visualalert (e.g., based on different threshold locations having differentassociated light patterns). On the other hand, if the object 1234 isdetected (e.g., by the planner system) as responding to the visual alert(e.g., object 1234 has altered its location to avoid a collision), thena light pattern configured to de-escalate the urgency of the visualalert may be selected. For example, one or more of light color, lightintensity, light pattern or some combination of the foregoing, may bealtered to indicate the de-escalation of the visual alert.

Planner system 310 may select light emitters 1202 based on anorientation of the autonomous vehicle 100 relative to a location of theobject 1234. For example, if the object 1234 is approaching theautonomous vehicle 100 head-on, then one or more light emitters 1202positioned external to the vehicle 100 that are approximately facing thedirection of the object's approach may be activated to emit light L forthe visual alert. As the relative orientation between the vehicle 100and the object 1234 changes, the planner system 310 may activate otheremitters 1202 positioned at other exterior locations on the vehicle 100to emit light L for the visual alert. Light emitters 1202 that may notbe visible to the object 1234 may not be activated to prevent potentialdistraction or confusion in other drivers, pedestrians or the like, atwhom the visual alert is not being directed.

FIG. 12B depicts profile views of examples of light emitter 1202positioning on an exterior of an autonomous vehicle 100. In example1230, a partial profile view of a first end of the vehicle 100 (e.g., aview along direction of arrow 1236) depicts several light emitters 1202disposed at different positions external to the vehicle 100. The firstend of the vehicle 100 may include lights 1232 that may be configuredfor automotive signaling functions, such as brake lights, turn signals,hazard lights, head lights, running lights, etc. In some examples, lightemitters 1202 may be configured to emit light L that is distinct (e.g.,in color of light, pattern of light, intensity of light, etc.) fromlight emitted by lights 1232 so that the function of light emitters 1202(e.g., visual alerts) is not confused with the automotive signalingfunctions implemented by lights 1232. In some examples, lights 1232 maybe configured to implement automotive signaling functions and visualalert functions. Light emitters 1202 may be positioned in a variety oflocations including but not limited to pillar sections, roof 100 u,doors, bumpers, and fenders, for example. In some examples, one or moreof the light emitters 1202 may be positioned behind an opticallytransparent or partially transparent surface or structure of the vehicle100, such as behind a window, a lens, or a covering, etc., for example.Light L emitted by the light emitter 1202 may pass through the opticallytransparent surface or structure and into the environment.

In example 1235, a second end of the vehicle 100 (e.g., view alongdirection opposite of arrow 1236) may include lights 1231 that may beconfigured for traditional automotive signaling functions and/or visualalert functions. Light emitters 1202 depicted in example 1235 may alsobe positioned in a variety of locations including but not limited topillar sections, roof 100 u, doors, bumpers, and fenders, for example.

Note that according to some examples, lights 1231 and 1232 may beoptional, and the functionality of automotive signaling functions, suchas brake lights, turn signals, hazard lights, head lights, runninglights, etc., may be performed by any one of the one or more lightemitters 1202.

FIG. 12C depicts a top plan view of one example 1240 of light emitter1202 activation based on an orientation of an autonomous vehiclerelative to an object. In FIG. 12C, object 1234 has a predicted locationLo relative to a location of the autonomous vehicle 100. A relativeorientation of the autonomous vehicle 100 with the object 1234 mayprovide complete sensor coverage of the object 1234 using sensor suites820 positioned to sense quadrants 2 and 3 and partial sensor coverage inquadrant 1. Based on the relative orientations of the vehicle 100 andthe object 1234, a sub-set of the light emitters denoted as 1202 a(e.g., on a side and an end of the vehicle 100) may be visuallyperceptible by the object 1234 and may be activated to emit light L intoenvironment 1290.

FIG. 12D depicts a profile view of one example 1245 of light emitteractivation based on an orientation of an autonomous vehicle relative toan object. In FIG. 12D, object 1234 has a predicted location Lo relativeto a location of the autonomous vehicle 100. Based on the relativeorientations of the vehicle 100 and the object 1234, a sub-set of thelight emitters denoted as 1202 a (e.g., on a side of the vehicle 100)may be visually perceptible by the object 1234 and may be activated toemit light L into environment 1290. In the example of FIG. 12D, therelative orientation of the vehicle 100 with the object 1234 isdifferent than depicted in FIG. 12C as the approach of the object 1234is not within the sensor coverage of quadrant 1. Accordingly, lightemitters 1202 positioned at an end of the vehicle 100 may not bevisually perceptible to the object 1234 and may not be activated for avisual alert.

FIG. 12E depicts one example of a flow diagram 1250 for implementing avisual alert from a light emitter in an autonomous vehicle. At a stage1252, data representing a trajectory of an autonomous vehicle in anenvironment external to the autonomous vehicle may be calculated basedon data representing a location of the autonomous vehicle in theenvironment. At a stage 1254, data representing a location of an objectin the environment may be determined. The object may include an objecttype. At a stage 1256, a predicted location of the object in theenvironment may be predicted based on the object type and the objectlocation. At a stage 1258, data representing a threshold location in theenvironment associated with a visual alert may be estimated based on thetrajectory of the autonomous vehicle and the predicted location of theobject. At a stage 1260, data representing a light pattern associatedwith the threshold location may be selected. At a stage 1262, thelocation of the object being coincident with the threshold location maybe detected. At a stage 1264, data representing an orientation of theautonomous vehicle relative to the location of the object may bedetermined. At a stage 1266, one or more light emitters of theautonomous vehicle may be selected based on the orientation of theautonomous vehicle relative to the location of the object. At a stage1268, selected light emitters may be caused (e.g., activated, triggered)to emit light indicative of the light pattern into the environment toimplement the visual alert.

FIG. 12F depicts a top plan view of one example 1270 of a light emitter1202 of an autonomous vehicle 100 emitting light L to implement a visualalert in concert with optional acoustic alerts from one or more arrays102. In FIG. 12F, autonomous vehicle 100 has a trajectory Tav along aroadway between lane markers denoted by dashed lines 1277, a detectedobject 1272 has a predicted location Lo that is approximately parallelto and in an opposite direction to the trajectory Tav. A planner systemof autonomous vehicle 100 may estimate three threshold locations T-1,T-2 and T-3 to implement a visual alert. Light emitters 1202 positionedat an end of the vehicle 100 in a direction of travel 1279 (e.g., lightemitters 1202 are facing the object 1272) are selected for the visualalert. In contrast, arrays 1202 positioned at the other end of thevehicle 100 that are not facing the direction of travel 1279, may not beselected for the visual alert because they may not be visuallyperceptible by the object 1272 and the may confuse other drivers orpedestrians, for example. Light patterns 1204 a, 1204 b and 1204 c maybe associated with threshold locations T-1, T-2 and T-3, respectively,and may be configured to implement escalating visual alerts (e.g.,convey increasing urgency) as the predicted location Lo of the object1272 gets closer to the location of the vehicle 100.

Further to FIG. 12F, another detected object 1271 may be selected by theplanner system of the vehicle 100 for an acoustic alert as describedabove in reference to FIGS. 11A-11C. Estimated threshold locationst-1-t-3 for object 1271 may be different than those for object 1272(e.g., T-1, T-2 and T-3). For example, acoustic alerts may be audiblyperceptible at a greater distance from the vehicle 100 than visualperception of visual alerts. A velocity or speed of object 1271 may begreater than a velocity or speed of object 1272 (e.g., an automobile vs.a bicycle) such that the threshold locations t-1-t-3 are placed furtherout due to a higher velocity of the object 1271 in order to providesufficient time for the object 1271 to alter its location to avoid acollision and/or for the vehicle 100 to take evasive maneuvers and/oractivate one or more of its safety systems. Although FIG. 12F depicts anexample of a visual alert associated with object 1272 and an acousticalert associated with object 1271, the number and type of safety systemactivated (e.g., interior, exterior or drive system) to address behaviorof objects in the environment are not limited to the example depictedand one or more safety systems may be activated. As one example, anacoustic alert, a visual alert, or both, may be communicated to object1271, object 1272, or both. As another example, one or more bladders maybe deployed and one or more seat belts may be tensioned as object 1271,object 1272, or both are close to colliding with or coming within anunsafe distance of the vehicle 100 (e.g., a predicted impact time isabout 2 seconds or less away).

The planner system may predict one or more regions of probable locations(e.g., 565 in FIGS. 5 and 6) of the objects in the environment based onpredicted motion of each object and/or predicted location of eachobject. The regions of probable locations and the size, number, spacing,and shapes of threshold locations within the regions of probablelocations may vary based on many factors including but not limited toobject characteristics (e.g., speed, type, location, etc.), the type ofsafety system selected, the velocity of the vehicle 100, and thetrajectory of the vehicle 100, for example.

FIG. 13A depicts one example 1300 of a bladder system in an exteriorsafety system of an autonomous vehicle. Exterior data 325 received bythe bladder system 369 may include data representing bladder selection1312, data representing bladder deployment 1314, and data representingbladder retraction 1316 (e.g., from a deployed state to an un-deployedstate). A processor 1305 may process exterior data 325 to control abladder selector coupled with one or more bladder engines 1311. Eachbladder engine 1311 may be coupled with a bladder 1310 as denoted byBladder-1 through Bladder-n. Each bladder 1310 may be activated, by itsrespective bladder engine 1311, from an un-deployed state to a deployedstate. Each bladder 1310 may be activated, by its respective bladderengine 1311, from the deployed state to the un-deployed state. In thedeployed state, a bladder 1310 may extend outward of the autonomousvehicle 100 (e.g., outward of a body panel or other structure of thevehicle 100). Bladder engine 1311 may be configured to cause a change involume of its respective bladder 1310 by forcing a fluid (e.g., apressurized gas) under pressure into the bladder 1310 to cause thebladder 1310 to expand from the un-deployed position to the deployedposition. A bladder selector 1317 may be configured to select whichbladder 1310 (e.g., in Bladder-1 through Bladder-n) to activate (e.g.,to deploy) and which bladder 1310 to de-activate (e.g., return to theun-deployed state). A selected bladder 1310 may be deployed upon theprocessor receiving the data representing bladder deployment 1314 forthe selected bladder 1310. A selected bladder 1310 may be returned tothe un-deployed state upon the processor receiving the data representingbladder retractions 1316 for the selected bladder 1310. The processor1305 may be configured to communicate data to bladder selector 1317 tocause the bladder selector 1317 to deploy a selected bladder 1310 (e.g.,via its bladder engine 1311) or to return a deployed bladder 1310 to theun-deployed state (e.g., via its bladder engine 1311).

A bladder 1310 may made be made from a flexible material, a resilientmaterial, an expandable material, such as rubber or a syntheticmaterial, or any other suitable material, for example. In some examples,a material for the bladder 1310 may be selected based on the materialbeing reusable (e.g., if a predicted impact to the bladder 1310 does notoccur or the collision occurs but does not damage the bladder 1310). Asone example, the bladder 1310 may be made from a material used for airsprings implemented in semi-tractor-trailer trucks. Bladder engine 1311may generate a pressurized fluid that may be introduced into the bladder1310 to expand the bladder 1310 to the deployed position or couple thebladder 1310 with a source of pressurized fluid, such a tank ofpressurized gas or a gas generator. Bladder engine 1311 may beconfigured to release (e.g., via a valve) the pressurized fluid from thebladder 1310 to contract the bladder 1310 from the deployed positionback to the un-deployed position (e.g., to deflate the bladder 1310 tothe un-deployed position). As one example, bladder engine 1311 may ventthe pressurized fluid in its respective bladder 1310 to atmosphere. Inthe deployed position, the bladder 1310 may be configured to absorbforces imparted by an impact of an object with the autonomous vehicle100, thereby, reducing or preventing damage to the autonomous vehicleand/or its passengers. For example, the bladder 1310 may be configuredto absorb impact forces imparted by a pedestrian or a bicyclist thatcollides with the autonomous vehicle 100.

Bladder data 1319 may be accessed by one or more of processor 1305,bladder selector 1317 or bladder engines 1311 to determine bladdercharacteristics, such as bladder size, bladder deployment time, bladderretraction time (e.g., a time to retract the bladder 1310 from thedeployed position back to the un-deployed position); the number ofbladders, the locations of bladders disposed external to the vehicle100, for example. Bladders 1310 may vary in size and location on theautonomous vehicle 100 and therefore may have different deployment times(e.g., an inflation time) and may have different retraction times (e.g.,a deflation time). Deployment times may be used in determining if thereis sufficient time to deploy a bladder 1310 or multiple bladders 1310based on a predicted time of impact of an object being tracked by theplanner system 310, for example. Bladder engine 1311 may includesensors, such as a pressure sensor to determine pressures in a bladder1310 when deployed and when un-deployed, and to determine if an impacthas ruptured or otherwise damaged a bladder 1310 (e.g., a rupturecausing a leak in the bladder 1310). The bladder engine 1311 and/orsensor system 320 may include a motion sensor (e.g., an accelerometer,MOT 888 in FIG. 8) to detect motion from an impact to the autonomousvehicle 100. A bladder 1310 and/or its respective bladder engine 1311that is not damaged by an impact may be reused. In the event thepredicted impact does not occur (e.g., there is no collision between thevehicle 100 and an object) the bladder 1310 may be returned to theun-deployed state (e.g., via bladder engine 1311) and the bladder 1310may be subsequently reused at a future time.

Further to FIG. 13A, an object 1334 having predicted location Lo may beapproaching the autonomous vehicle 100 from one of its sides such that atrajectory Tav of the autonomous vehicle 100 is approximatelyperpendicular to a predicted object path of the object 1334. Object 1334may be predicted to impact the side of the autonomous vehicle 100, andexterior data 325 may include data configured to cause bladder selector1311 to select one or more bladders 1310 positioned on the side of theautonomous vehicle 100 (e.g., the side on which the impact is predictedto occur) to deploy in advance of the predicted impact.

FIG. 13B depicts examples 1330 and 1335 of bladders in an exteriorsafety system of an autonomous vehicle. In example 1330, vehicle 100 mayinclude several bladders 1310 having positions associated with externalsurfaces and/or structures of the vehicle 100. The bladders 1310 mayhave different sizes and shapes. Bladders 1310 may be concealed behindother structures of the vehicle or may be disguised with ornamentationor the like. For example, a door panel or fender of the vehicle 100 mayinclude a membrane structure and the bladder 1310 may be positionedbehind the membrane structure. Forces generated by deployment of thebladder 1310 may rupture the membrane structure and allow the bladder toexpand outward in a direction external to the vehicle 100 (e.g., deployoutward into the environment). Similarly, in example 1335, vehicle 100may include several bladders 1310. The bladders depicted in examples1330 and 1335 may be symmetrical in location and/or size on the vehicle100.

FIG. 13C depicts examples 1340 and 1345 of bladder deployment in anautonomous vehicle. In example 1340, an object 1371 (e.g., a car) is ona predicted collision course with vehicle 100 and has a predictedlocation of Lo. Autonomous vehicle 100 has a trajectory Tav. Based on arelative orientation of the vehicle 100 with the object 1371, apredicted impact location on the vehicle 100 is estimated to be inquadrants 2 and 3 (e.g., impact to a side of the vehicle 100). Twopassengers P located in the interior 100 i of the vehicle 100 may be atrisk due to a crumple zone distance z1 measured from the side of vehicle100 to the passenger P seating position in the interior 100 i. Plannersystem 310 may compute an estimated time to impact Timpact for theobject 1371 (e.g., using kinematics calculator 384) to determine whetherthere is sufficient time to maneuver the autonomous vehicle 100 to avoidthe collision or to reduce potential injury to passengers P. If theplanner system 310 determines that the time to maneuver the vehicle 100is less than the time of impact (e.g., Tmanuever<Timpact), the plannersystem may command the drive system 326 to execute an avoidance maneuver1341 to rotate the vehicle 100 in a clockwise direction of the arrow(e.g., arrow for avoidance maneuver 1341) to position the end of thevehicle 100 in the path of the object 1371. Planner system may alsocommand activation of other interior and exterior safety systems tocoincide with the avoidance maneuver 1341, such as seat belt tensioningsystem, bladder system, acoustic beam-steering arrays 102 and lightemitters 1202, for example.

In example 1345, the vehicle 100 has completed the avoidance maneuver1341 and the object 1371 is approaching from an end of the vehicle 100instead of the side of the vehicle 100. Based on the new relativeorientation between the vehicle 100 and the object 1371, the plannersystem 310 may command selection and deployment of a bladder 1310′positioned on a bumper at the end of the vehicle 100. If an actualimpact occurs, the crumple zone distance has increased from z1 to z2(e.g., z2>z1) to provide a larger crumple zone in the interior 100 i ofthe vehicle 100 for passengers P. Prior to the avoidance maneuver 1341,seat belts worn by passengers P may be been pre-tensioned by the seatbelt tensioning system to secure the passengers P during the maneuver1341 and in preparation for the potential impact of the object 1371.

In example 1345, a deployment time of the bladder 1310′, Tdeploy, isdetermined to be less (e.g., by planner system 310) than the predictedimpact time, Timpact, of the object 1371. Therefore, there is sufficienttime to deploy bladder 1310′ prior to a potential impact of the object1371. A comparison of activation times and impact times may be performedfor other safety systems such as the seat belt tensioning system, seatactuator system, acoustic arrays and light emitters, for example.

The drive system (e.g., 326 in FIG. 3B) may be commanded (e.g., viaplanner system 310) to rotate the wheels of the vehicle (e.g., via thesteering system) and orient the vehicle 100 (e.g., via the propulsionsystem) to the configuration depicted in example 1345. The drive systemmay be commanded (e.g., via planner system 310) to apply brakes (e.g.,via the braking system) to prevent the vehicle 100 from colliding withother objects in the environment (e.g., prevent the vehicle 100 frombeing pushed into other objects as a result of forces from the impact).

FIG. 14 depicts one example 1400 of a seat belt tensioning system in aninterior safety system 322 of an autonomous vehicle 100. In FIG. 14,interior data 323 may be received by seat belt tensioning system 361.Interior data 323 may include but is not limited to data representing abelt tensioner select signal 1412, a seat belt tension trigger signal1414, and a seat belt tension release signal 1416. The belt tensionerselect signal 1412 may select one or more belt tensioners 1411, witheach belt tensioner 1411 including a seat belt 1413 (also denoted asB-1, B-2 to B-n). Each belt 1413 may be mechanically coupled with atensioning mechanism (not shown) in the belt tensioner 1411. Thetensioning mechanism may include a reel configured to reel in a portionof the belt on the reel the belt 1413 is wrapped around to take theslack out of the belt 1413. A belt tensioner 1411 selected by the belttensioner select signal 1412 may upon receiving the seat belt tensiontrigger signal 1414, apply tension to its respective belt 1413. Forexample, belt tensioner 1411 having belt 1413 (B-2) may actuate belt B-2from a slack state (e.g., not tightly coupled to a passenger wearingbelt B-2) to a tension state (denoted by dashed line). In the tensionstate, belt B-2 may apply pressure to a passenger that may tightlycouple the passenger to the seat during an avoidance maneuver and/or inanticipation of a predicted collision with an object, for example.

The belt B-2 may be returned from the tension state to and back to theslack state by releasing the seat belt tension trigger signal 1414 or byreleasing the seat belt tension trigger signal 1414 followed byreceiving the seat belt tension release signal 1416. A sensor (e.g., apressure or force sensor) (not shown) may detect whether a seat in theautonomous vehicle is occupied by a passenger and may allow activationof the belt tensioner select signal 1412 and/or the seat belt tensiontrigger signal 1414 if the sensor indicates the seat is occupied. If theseat sensor does not detect occupancy of a seat, then the belt tensionerselect signal 1412 and/or the seat belt tension trigger signal 1414 maybe de-activated. Belt data 1419 may include data representing beltcharacteristics including but not limited to belt tensioning time, beltrelease times, and maintenance logs for the seat belts 1413 in seat beltsystem 361, for example. Seat belt system 361 may operate as a distinctsystem in autonomous vehicle 100 or may operate in concert with othersafety systems of the vehicle 100, such as the light emitter(s),acoustic array(s), bladder system, seat actuators, and the drive system,for example.

FIG. 15 depicts one example 1500 of a seat actuator system 363 in aninterior safety system 322 of an autonomous vehicle 100. In one example,the seat actuator system 363 may be configured to actuate a seat 1518(e.g., a Seat-1 through a Seat-n) from a first position in the interiorof the autonomous vehicle 100 to a second position in the interior ofthe autonomous vehicle 100 using energy from an impact force 1517imparted to the vehicle 100 from an object (not shown) in theenvironment 1590 due to a collision between the vehicle 100 and theobject (e.g., another vehicle). A force 1513 mechanically communicatedto the seat (e.g., Seat-n) from the impact force 1517, may move the seatfrom the first position (e.g., near an end of the vehicle 100) to asecond position (e.g., towards the center of the interior 100 i of thevehicle 100), for example. A counter acting force c-force 1515 may beapplied to the seat (e.g., Seat-n) to control acceleration forces causedby the force 1513 (note, that while not shown, the directions of c-force1515 and force 1513 as depicted in FIG. 15 may be in substantiallycommon direction of impact force 1517). The counter acting force c-force1515 may be a spring being configured to compress to counteract force1513 or being configured to stretch to counteract force 1513. In otherexamples, counter acting force c-force 1515 may be generated by a damper(e.g., a shock absorber) or by an air spring, for example. A mechanismthat provides the counter acting force c-force 1515 may be coupled to aseat coupler 1511 and to the seat (e.g., Seat-n). The seat coupler 1511may be configured to anchor the mechanism that provides the counteracting force c-force 1515 to a chassis or other structure of the vehicle100. As the impact force 1517 causes mechanical deformation of thestructure of the vehicle 100 (e.g., collapses a crumple zone), a ram orother mechanical structure coupled to the seat may be urged forward(e.g., from the first position to the second position) by the deformingstructure of the vehicle to impart the force 1513 to the seat, while thecounter acting force c-force 1515 resists the movement of the seat fromthe first position to the second position.

In other examples, seat coupler 1511 may include an actuator toelectrically, mechanically, or electromechanically actuate a seat 1518(e.g., the Seat-n) from the first position to the second position inresponse to data representing a trigger signal 1516. Seat coupler 1511may include a mechanism (e.g., a spring, a damper, an air spring, adeformable structure, etc.) that provides the counter acting forcec-force 1515. Data representing a seat select 1512 and data representingan arming signal 1514 may be received by a processor 1505. A seatselector 1519 may select one or more of the seat couplers 1511 based onthe data representing the seat select 1512. Seat selector 1519 may notactuate a selected seat until the data representing the arming signal1514 is received. The data representing the arming signal 1514 may beindicative of a predicted collision with the vehicle 100 having a highprobability of occurring (e.g., an object based on its motion andlocation is predicted to imminently collide with the vehicle). The datarepresenting the arming signal 1514 may be used as a signal to activatethe seat belt tensioning system. Seats 1518 (e.g., seat-1 throughseat-n) may be seats that seat a single passenger (e.g., a bucket seat)or that seat multiple passengers (e.g., a bench seat), for example. Theseat actuator system 363 may act in concert with other interior andexterior safety systems of the vehicle 100.

FIG. 16A depicts one example 1600 of a drive system 326 in an autonomousvehicle 100. In FIG. 16A, drive data 327 communicated to drive system326 may include but is not limited to data representing steering control1612, braking control 1614, propulsion control 1618 and signal control1620. Drive data 327 may be used for normal driving operations of theautonomous vehicle 100 (e.g., picking up passengers, transportingpassengers, etc.), but also may be used to mitigate or avoid collisionsand other potentially dangerous events related to objects in anenvironment 1690.

Processor 1605 may communicate drive data 327 to specific drive systems,such as steering control data to the steering system 361, brakingcontrol data to the braking system 364, propulsion control data to thepropulsion system 368 and signaling control data to the signaling system362. The steering system 361 may be configured to process steering datato actuate wheel actuators WA-1 through WA-n. Vehicle 100 may beconfigured for multi-wheel independent steering (e.g., four wheelsteering). Each wheel actuator WA-1 through WA-n may be configured tocontrol a steering angle of a wheel coupled with the wheel actuator.Braking system 364 may be configured to process braking data to actuatebrake actuators BA-1 through BA-n. Braking system 364 may be configuredto implement differential braking and anti-lock braking, for example.Propulsion system 368 may be configured to process propulsion data toactuate drive motors DM-1 through DM-n (e.g., electric motors).Signaling system 362 may process signaling data to activate signalelements S-1 through S-n (e.g., brake lights, turn signals, headlights,running lights, etc.). In some examples, signaling system 362 may beconfigured to use one or more light emitters 1202 to implement asignaling function. For example, signaling system 362 may be configuredto access all or a portion of one or more light emitters 1202 toimplement a signaling function (e.g., brake lights, turn signals,headlights, running lights, etc.).

FIG. 16B depicts one example 1640 of obstacle avoidance maneuvering inan autonomous vehicle 100. In FIG. 16B, an object 1641 (e.g., anautomobile) has a predicted location Lo that is in conflict withtrajectory Tav of autonomous vehicle 100. The planner system maycalculate that there is not sufficient time to use the drive system toaccelerate the vehicle 100 forward to a region 1642 located alongtrajectory Tav because the object 1641 may collide with the vehicle 100;therefore, region 1642 may not be safely maneuvered into and the region1642 may be designated (e.g., by the planner system) as a blocked region1643. However, the planner system may detect (e.g., via sensor datareceived by the perception system and map data from the localizersystem) an available open region in the environment around the vehicle100. For example, a region 1644 may be safely maneuvered into (e.g.,region 1644 has no objects, moving or static, to interfere with thetrajectory of the vehicle 100). The region 1644 may be designated (e.g.,by the planner system) as an open region 1645. The planner system maycommand the drive system (e.g., via steering, propulsion, and brakingdata) to alter the trajectory of the vehicle 100 from its originaltrajectory Tav to an avoidance maneuver trajectory Tm, to autonomouslynavigate the autonomous vehicle 100 into the open region 1645.

In concert with obstacle avoidance maneuvering, the planner system mayactivate one or more other interior and/or exterior safety systems ofthe vehicle 100, such causing the bladder system to deploy bladders onthose portions of the vehicle that may be impacted by object 1641 if theobject 1641 changes velocity or in the event the obstacle avoidancemaneuver is not successful.

The seat belt tensioning system may be activated to tension seat beltsin preparation for the avoidance maneuver and to prepare for a potentialcollision with the object 1641. Although not depicted in FIG. 16B, othersafety systems may be activated, such as one or more acoustic arrays 102and one or more light emitters 1202 to communicate acoustic alerts andvisual alerts to object 1641. Belt data and bladder data may be used tocompute tensioning time for seat belts and bladder deployment time forbladders and those computed times may be compared to an estimated impacttime to determine if there is sufficient time before an impact totension belts and/or deploy bladders (e.g., Tdeploy<Timpact and/orTtension<Timpact). Similarly, the time necessary for drive system toimplement the maneuver into the open region 1645 may compared to theestimated impact time to determine if there is sufficient time toexecute the avoidance maneuver (e.g., Tmaneuver<Timpact).

FIG. 16C depicts another example 1660 of obstacle avoidance maneuveringin an autonomous vehicle 100. In FIG. 16C, an object 1661 has apredicted location Lo that may result in a collision (e.g., arear-ending) of autonomous vehicle 100. Planner system may determine apredicted impact zone (e.g., a region or probabilities where a collisionmight occur based on object dynamics). Prior to a predicted collisionoccurring, planner system may analyze the environment around the vehicle100 (e.g., using the overlapping sensor fields of the sensor in thesensor system) to determine if there is an open region the vehicle 100may be maneuvered into. The predicted impact zone is behind the vehicle100 and is effectively blocked by the predicted location Lo having avelocity towards the location of the vehicle 100 (e.g., the vehiclecannot safely reverse direction to avoid the potential collision). Thepredicted impact zone may be designated (e.g., by the planner system) asa blocked region 1681. Traffic lanes to the left of the vehicle 100 lackan available open region and are blocked due to the presence of threeobjects 1663, 1665 and 1673 (e.g., two cars located in the adjacenttraffic lane and one pedestrian on a sidewalk). Accordingly, the regionmay be designated as blocked region 1687. A region ahead of the vehicle100 (e.g., in the direction of trajectory Tav) is blocked due to theapproach of a vehicle 1667 (e.g., a large truck). A location to theright of the vehicle 100 is blocked due to pedestrians 1671 on asidewalk at that location. Therefore, those regions may be designated asblocked region 1683 and 1689, respectively. However, a region that isfree of objects may be detected (e.g., via the planner system andperception system) in a region 1691. The region 1691 may be designatedas an open region 1693 and the planner system may command the drivesystem to alter the trajectory from trajectory Tav to an avoidancemaneuver trajectory Tm. The resulting command may cause the vehicle 100to turn the corner into the open region 1693 to avoid the potentialrear-ending by object 1661.

In concert with the avoidance maneuver into the open region 1693, othersafety systems may be activated, such as bladders 1310 on an end of thevehicle 100, seat belt tensioners 1411, acoustic arrays 102, seatactuators 1511, and light emitters 1202, for example. As one example, asthe vehicle 1661 continues to approach the vehicle 100 on predictedlocation Lo, one or more bladders 1301 may be deployed (e.g., at a timeprior to a predicted impact time to sufficiently allow for bladderexpansion to a deployed position), an acoustic alert may be communicated(e.g., by one or more acoustic beam-steering arrays 102), and a visualalert may be communicated (e.g., by one or more light emitters 1202).

Planner system may access data representing object types (e.g. data onvehicles such as object 1661 that is predicted to rear-end vehicle 100)and compare data representing the object with the data representing theobject types to determine data representing an object type (e.g.,determine an object type for object 1661). Planner system may calculatea velocity or speed of an object based data representing a location ofthe object (e.g., track changes in location over time to calculatevelocity or speed using kinematics calculator 384). Planner system mayaccess a data store, look-up table, a data repository or other datasource to access data representing object braking capacity (e.g.,braking capacity of vehicle 1661). The data representing the object typemay be compared with the data representing object braking capacity todetermine data representing an estimated object mass and datarepresenting an estimated object braking capacity. The data representingthe estimated object mass and the data representing the estimated objectbraking capacity may be based on estimated data for certain classes ofobjects (e.g., such as different classes of automobiles, trucks,motorcycles, etc.). For example, if object 1661 has an object typeassociated with a mid-size four door sedan, than an estimated grossvehicle mass or weight that may be an average for that class of vehiclemay represent the estimated object mass for object 1661. The estimatedbraking capacity may also be an averaged value for the class of vehicle.

Planner system may calculate data representing an estimated momentum ofthe object based on the data representing the estimated object brakingcapacity and the data representing the estimated object mass. Theplanner system may determine based on the data representing theestimated momentum, that the braking capacity of the object (e.g.,object 1661) is exceeded by its estimated momentum. The planner systemmay determine based on the momentum exceeding the braking capacity, tocompute and execute the avoidance maneuver of FIG. 16C to move thevehicle 100 away from the predicted impact zone and into the open region1693.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described conceptualtechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described conceptualtechniques. The disclosed examples are illustrative and not restrictive.

What is claimed is:
 1. A method comprising: receiving trajectory datarepresenting a trajectory of an autonomous vehicle in an environmentexternal to the autonomous vehicle; determining object data comprising aposition of an object in the environment, an object type of the object,and an object classification of the object; determining a predictedlocation of the object in the environment based at least in part on theobject data; determining that the predicted location of the objectcoincides with the trajectory; determining a first threshold locationalong the trajectory based at least in part on the predicted location ofthe object and the object data; determining a second threshold locationalong the trajectory based at least in part on the predicted location ofthe object and the object data; sensing an updated object position ofthe object in the environment; implementing a safety action using asafety system based at least in part on the updated object position, thesafety action associated with the first threshold location or the secondthreshold location.
 2. The method of claim 1, further comprising:determining a probability of impact of the object with the autonomousvehicle, wherein at least one of the first threshold location or thesecond threshold location is determined based at least in part on theprobability of impact.
 3. The method of claim 2, wherein the probabilityof impact is used to determine at least one of a first distance of thefirst threshold location from the autonomous vehicle or a seconddistance of the second threshold location from the autonomous vehicle.4. The method of claim 1, wherein the safety system comprises anacoustic beam steering array, and further wherein the safety actioncomprises a first safety action and a second safety action, the firstsafety action including emitting a first audio alert at a first anglerelative to the trajectory and the second safety action comprisesemitting a second audio alert at a second angle relative to thetrajectory, the second audio alert differing from the first audio alert,the first safety action associated with the first threshold location,and the second safety action associated with the second thresholdlocation.
 5. The method of claim 1, wherein the safety action includes afirst safety action that comprises emitting a first light pattern and asecond safety action that comprises emitting a second light pattern, thefirst safety action associated with the first threshold location and thesecond safety action associated with the second threshold location. 6.The method of claim 1, further comprising repeatedly sensing an updatedobject position, wherein the implementing the safety action comprisestensioning a seat belt.
 7. The method of claim 1, further comprisingdetermining a collision avoidance trajectory to maneuver the autonomousvehicle from the trajectory.
 8. A system comprising: an autonomousvehicle, the autonomous vehicle having a plurality of sensors forsensing an object in an environment surrounding the autonomous vehicle;and a computing system communicatively coupled to the autonomous vehicleto receive data from the plurality of sensors and to transmitinstructions to the autonomous vehicle, the computing system beingprogrammed to: determine object data comprising a position of theobject, an object type of the object, and an object classification ofthe object; determine a predicted location of the object in theenvironment based at least in part on the object data; determine thatthe predicted location of the object coincides with a trajectory of theautonomous vehicle; determine a first threshold location along thetrajectory based at least in part on the predicted location of theobject and the object data; determine a second threshold location alongthe trajectory based at least in part on the predicted location of theobject and the object data; cause the autonomous vehicle to emit a firstalert based at least in part on a determination that an updated locationof the object coincides with the first threshold boundary; and cause theautonomous vehicle to emit a second alert based at least in part on adetermination that the updated location of the object coincides with thesecond threshold boundary.
 9. The system of claim 8, wherein thecomputing system is further programmed to determine a probability ofimpact of the object with the autonomous vehicle, wherein at least oneof the threshold location or the second threshold location is determinedbased at least in part on the probability of impact.
 10. The method ofclaim 9, wherein the probability of impact is used to determine at leastone of a first distance of the first threshold location from theautonomous vehicle or a second distance of the second threshold locationfrom the autonomous vehicle.
 11. The system of claim 8, wherein theautonomous vehicle is a bi-directional vehicle configured to driveforward in a first direction or drive forward in a substantiallyopposite second direction without turning around the autonomous vehicle,and the computing system is further programmed to generate instructionsfor the autonomous vehicle to change direction from driving forward inthe first direction along the trajectory to driving forward in thesecond direction without turning around based on a determination thatthe updated location of the object coincides with the second thresholdboundary.
 12. The system of claim 8, wherein the safety system comprisesan acoustic beam steering array, the first safety action comprisesemitting a first audio alert at a first angle relative to the trajectoryand the second safety action comprises emitting a second audio alert ata second angle relative to the trajectory, the second audio alert beingdifferent from the first audio alert.
 13. The system of claim 8, whereinthe safety system comprises a light emitter, the first safety actioncomprises emitting a first light pattern, and the second safety actioncomprises emitting a second light pattern, the second light patternbeing different from the first light pattern.
 14. A method comprising:receiving trajectory data representing a trajectory of an autonomousvehicle in an environment external to the autonomous vehicle;determining object data comprising a position of an object in theenvironment, an object type of the object, and an object classificationof the object; determining a probability of impact of the autonomousvehicle and the object based at least in part on the trajectory and theobject data; based at least in part on the probability of impact,determining a first threshold location along the trajectory and a secondthreshold location along the trajectory; sensing an updated objectposition of the object in the environment; determining an updatedvehicle position along the trajectory; and implementing a first safetyaction using a safety system in response to the updated object positionand updated vehicle position corresponding to the first thresholdlocation, or implementing a second safety action using the safety systemin response to the updated vehicle position corresponding to the secondthreshold location.
 15. The method of claim 14, further comprisingdetermining a predicted next location of the object based at least inpart on the object data, the predicted next location being further usedto determine the probability of impact.
 16. The method of claim 14,further comprising: determining a predicted location of a collisionbetween the object and the autonomous vehicle, the predicted locationbeing used to determine the probability of impact.
 17. The method ofclaim 14, wherein at least one of determining the first thresholdlocation comprises determining a first distance from the autonomousvehicle or determining the second threshold location comprisesdetermining a second distance from the autonomous vehicle.
 18. Themethod of claim 14, wherein the safety system comprises an acoustic beamsteering array, the first safety action comprises emitting a first audioalert at a first angle relative to the trajectory and the second safetyaction comprises emitting a second audio alert at a second anglerelative to the trajectory.
 19. The method of claim 14, wherein thesafety system comprises a light emitter, the first safety actioncomprises emitting a first light pattern, and the second safety actioncomprises emitting a second light pattern.
 20. The method of claim 14,wherein the object data further comprises an object behavior, the methodfurther comprising: determining an updated object behavior; andde-escalating the second safety action, based at least in part on theupdated object behavior.